Enviar pesquisa
Carregar
Notes from Coursera Deep Learning courses by Andrew Ng
•
3,946 gostaram
•
349,164 visualizações
Tess Ferrandez
Seguir
My notes from the excellent Coursera specialization by Andrew Ng
Leia menos
Leia mais
Tecnologia
Denunciar
Compartilhar
Denunciar
Compartilhar
1 de 28
Baixar agora
Baixar para ler offline
Recomendados
This Machine Learning presentation is ideal for beginners to learn Machine Learning from scratch. By the end of this presentation, you will learn why Machine Learning is so important in our lives, what is Machine Learning, the various types of Machine Learning (Supervised, Unsupervised and Reinforcement learning), how do we choose the right Machine Learning solution, what are the different Machine Learning algorithms and how do they work (with simple examples and use-cases). This Machine Learning presentation will cover the following topics: 1. Life without Machine Learning 2. Life with Machine Learning 3. What is Machine Learning 4. Machine Learning Process 5. Types of Machine Learning 6. Supervised Vs Unsupervised 7. The right Machine Learning solutions 8. Machine Learning Algorithms 9. Use case - Predicting the price of a house using Linear Regression What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning - - - - - -
Machine Learning Tutorial | Machine Learning Basics | Machine Learning Algori...
Machine Learning Tutorial | Machine Learning Basics | Machine Learning Algori...
Simplilearn
This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail. Below topics are explained in this Machine Learning tutorial part -2 : 1. Clustering - What is clustering? - K-Means clustering - Flowchart to understand K-Means clustering - Demo - Clustering of cars based on brands 2. Logistic regression - What is logistic regression? - Logistic regression curve & Sigmoid function - Demo - Classify a tumor as malignant or benign based on features About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...
Simplilearn
This Machine Learning With Python presentation gives an introduction to Machine Learning and how to implement machine learning algorithms in Python. By the end of this presentation you will be able to understand Machine Learning workflow, steps to download Anaconda, types of Machine Learning and application of these in a demo showcasing Linear Regression and K-Means clustering. Below are the topics covered in this Machine Learning presentation: 1. Why Machine Learning? 2. Applications of Machine Learning 3. How does Machine Learning work? 4. Machine Learning Workflow 5. Steps to download Anaconda 6. Types of Machine Learning 7. Linear Regression Demo 8. K-Means Clustering Demo 9. Use Case - Weather Analysis - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning - - - - - -
Machine Learning With Python | Machine Learning Algorithms | Machine Learning...
Machine Learning With Python | Machine Learning Algorithms | Machine Learning...
Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is. Below topics are explained in this Deep Learning Presentation: 1. What is Deep Learning? 2. Why do we need Deep Learning? 3. Applications of Deep Learning 4. What is Neural Network? 5. Activation Functions 6. Working of Neural Network Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist. Why Deep Learning? It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals: 1. Software engineers 2. Data scientists 3. Data analysts 4. Statisticians with an interest in deep learning
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...
Simplilearn
딥러닝에 대한 기본 개념 소개
Introduction of Deep Learning
Introduction of Deep Learning
Myungjin Lee
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters. Below topics are explained in this Machine Learning presentation: 1. Why Machine Learning? 2. What is Machine Learning? 3. Types of Machine Learning 4. Machine Learning Algorithms - Linear Regression - Decision Trees - Support Vector Machine 5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...
Simplilearn
An introduction to Machine Learning
An introduction to Machine Learning
butest
My slides on 'Introduction to Deep learning' presented for the first meeting of deep learning @ Leeds community.
Introduction to Deep learning
Introduction to Deep learning
leopauly
Recomendados
This Machine Learning presentation is ideal for beginners to learn Machine Learning from scratch. By the end of this presentation, you will learn why Machine Learning is so important in our lives, what is Machine Learning, the various types of Machine Learning (Supervised, Unsupervised and Reinforcement learning), how do we choose the right Machine Learning solution, what are the different Machine Learning algorithms and how do they work (with simple examples and use-cases). This Machine Learning presentation will cover the following topics: 1. Life without Machine Learning 2. Life with Machine Learning 3. What is Machine Learning 4. Machine Learning Process 5. Types of Machine Learning 6. Supervised Vs Unsupervised 7. The right Machine Learning solutions 8. Machine Learning Algorithms 9. Use case - Predicting the price of a house using Linear Regression What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning - - - - - -
Machine Learning Tutorial | Machine Learning Basics | Machine Learning Algori...
Machine Learning Tutorial | Machine Learning Basics | Machine Learning Algori...
Simplilearn
This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail. Below topics are explained in this Machine Learning tutorial part -2 : 1. Clustering - What is clustering? - K-Means clustering - Flowchart to understand K-Means clustering - Demo - Clustering of cars based on brands 2. Logistic regression - What is logistic regression? - Logistic regression curve & Sigmoid function - Demo - Classify a tumor as malignant or benign based on features About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...
Simplilearn
This Machine Learning With Python presentation gives an introduction to Machine Learning and how to implement machine learning algorithms in Python. By the end of this presentation you will be able to understand Machine Learning workflow, steps to download Anaconda, types of Machine Learning and application of these in a demo showcasing Linear Regression and K-Means clustering. Below are the topics covered in this Machine Learning presentation: 1. Why Machine Learning? 2. Applications of Machine Learning 3. How does Machine Learning work? 4. Machine Learning Workflow 5. Steps to download Anaconda 6. Types of Machine Learning 7. Linear Regression Demo 8. K-Means Clustering Demo 9. Use Case - Weather Analysis - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning - - - - - -
Machine Learning With Python | Machine Learning Algorithms | Machine Learning...
Machine Learning With Python | Machine Learning Algorithms | Machine Learning...
Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is. Below topics are explained in this Deep Learning Presentation: 1. What is Deep Learning? 2. Why do we need Deep Learning? 3. Applications of Deep Learning 4. What is Neural Network? 5. Activation Functions 6. Working of Neural Network Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist. Why Deep Learning? It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals: 1. Software engineers 2. Data scientists 3. Data analysts 4. Statisticians with an interest in deep learning
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...
Simplilearn
딥러닝에 대한 기본 개념 소개
Introduction of Deep Learning
Introduction of Deep Learning
Myungjin Lee
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters. Below topics are explained in this Machine Learning presentation: 1. Why Machine Learning? 2. What is Machine Learning? 3. Types of Machine Learning 4. Machine Learning Algorithms - Linear Regression - Decision Trees - Support Vector Machine 5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...
Simplilearn
An introduction to Machine Learning
An introduction to Machine Learning
butest
My slides on 'Introduction to Deep learning' presented for the first meeting of deep learning @ Leeds community.
Introduction to Deep learning
Introduction to Deep learning
leopauly
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
Recurrent Neural Networks, LSTM and GRU
Recurrent Neural Networks, LSTM and GRU
ananth
Machine Learning presentation.
Machine Learning presentation.
butest
Crop prediction using machine learning
Crop prediction using machine learning
Crop prediction using machine learning
dataalcott
This Artificial Intelligence presentation will introduce you to the AI world and give you a short glimpse about AI history. By the end of this video, you will learn what is Artificial intelligence, types of Artificial Intelligence, applications of Artificial Intelligence and future of Artificial Intelligence. The topics covered in this presentation are as follows: 1. Brief History of Artificial Intelligence 2. What is Artificial intelligence 3. Types of Artificial Intelligence 4. Applications of Artificial Intelligence 5. Future of Artificial Intelligence ----------------------------------------------------------------------------------------- Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming. Why learn Artificial Intelligence? The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills. You can gain in-depth knowledge of Artificial Intelligence by taking our Artificial Intelligence certification training course. Those who complete the course will be able to: Master the concepts of supervised and unsupervised learning Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project. Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning. Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems -----------------------------------------------------------------------------------------
What Is Artificial Intelligence? | Artificial Intelligence For Beginners | Wh...
What Is Artificial Intelligence? | Artificial Intelligence For Beginners | Wh...
Simplilearn
This K-Means clustering algorithm presentation will take you through the machine learning introduction, types of clustering algorithms, k-means clustering, how does K-Means clustering work and at least explains K-Means clustering by taking a real life use case. This Machine Learning algorithm tutorial video is ideal for beginners to learn how K-Means clustering work. Below topics are covered in this K-Means Clustering Algorithm presentation: 1. Types of Machine Learning? 2. What is K-Means Clustering? 3. Applications of K-Means Clustering 4. Common distance measure 5. How does K-Means Clustering work? 6. K-Means Clustering Algorithm 7. Demo: k-Means Clustering 8. Use case: Color compression - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - -
K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...
K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...
Simplilearn
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist). What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA). Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Deep learning - A Visual Introduction
Deep learning - A Visual Introduction
Lukas Masuch
This presentation about Deep Learning with Python will help you understand what is deep learning, applications of deep learning, what is a neural network, biological versus artificial neural networks, introduction to TensorFlow, activation function, cost function, how neural networks work, and what gradient descent is. Deep learning is a technology that is used to achieve machine learning through neural networks. We will also look into how neural networks can help achieve the capability of a machine to mimic human behavior. We'll also implement a neural network manually. Finally, we'll code a neural network in Python using TensorFlow. Below topics are explained in this Deep Learning with Python presentation: 1. What is Deep Learning 2. Biological versus Artificial Intelligence 3. What is a Neural Network 4. Activation function 5. Cost function 6. How do Neural Networks work 7. How do Neural Networks learn 8. Implementing the Neural Network 9. Gradient descent 10. Deep Learning platforms 11. Introduction to TensoFlow 12. Implementation in TensorFlow You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline 2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before 3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces 4. Build deep learning models in TensorFlow and interpret the results 5. Understand the language and fundamental concepts of artificial neural networks 6. Troubleshoot and improve deep learning models 7. Build your own deep learning project 8. Differentiate between machine learning, deep learning, and artificial intelligence There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals: 1. Software engineers 2. Data scientists 3. Data analysts 4. Statisticians with an interest in deep learning Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
Deep Learning With Python | Deep Learning And Neural Networks | Deep Learning...
Deep Learning With Python | Deep Learning And Neural Networks | Deep Learning...
Simplilearn
Practical Data Science. Slides of an 1 hour introductory talk about predictive modeling using Machine Learning with a focus on supervised learning.
An Introduction to Supervised Machine Learning and Pattern Classification: Th...
An Introduction to Supervised Machine Learning and Pattern Classification: Th...
Sebastian Raschka
Brief introduction on attention mechanism and its application in neural machine translation, especially in transformer, where attention was used to remove RNNs completely from NMT.
Notes on attention mechanism
Notes on attention mechanism
Khang Pham
This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work. Below topics are explained in this recurrent neural networks tutorial: 1. What is a neural network? 2. Popular neural networks? 3. Why recurrent neural network? 4. What is a recurrent neural network? 5. How does an RNN work? 6. Vanishing and exploding gradient problem 7. Long short term memory (LSTM) 8. Use case implementation of LSTM Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist. Why Deep Learning? It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results. And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: Learn more at: https://www.simplilearn.com/
Recurrent Neural Network (RNN) | RNN LSTM Tutorial | Deep Learning Course | S...
Recurrent Neural Network (RNN) | RNN LSTM Tutorial | Deep Learning Course | S...
Simplilearn
This Support Vector Machine (SVM) presentation will help you understand Support Vector Machine algorithm, a supervised machine learning algorithm which can be used for both classification and regression problems. This SVM presentation will help you learn where and when to use SVM algorithm, how does the algorithm work, what are hyperplanes and support vectors in SVM, how distance margin helps in optimizing the hyperplane, kernel functions in SVM for data transformation and advantages of SVM algorithm. At the end, we will also implement Support Vector Machine algorithm in Python to differentiate crocodiles from alligators for a given dataset. Below topics are explained in this Support Vector Machine presentation: 1. What is Machine Learning? 2. Why support vector machine? 3. What is support vector machine? 4. Understanding support vector machine 5. Advantages of support vector machine 6. Use case in Python - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - -
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Simplilearn
dddddddddddddddddddddddddddddddddddddddddddddddddddddddffffffffffffffffffffffffffffffffffffffffffffvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbcccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccojjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjppppppppppppppppppppppppppppppppppppooooooooooooooooooooooooooooooooooooooooooooojjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjpppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiikkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj
Deep learning.pptx
Deep learning.pptx
MdMahfoozAlam5
This slide first introduces the sequential pattern mining problem and also presents some required definitions in order to understand GSP algorithm. At then end there is a brief introduction of GSP algorithm and some practical constraints which it supports.
Sequential Pattern Mining and GSP
Sequential Pattern Mining and GSP
Hamidreza Mahdavipanah
Machine learning
Machine learning
Machine learning
Dr Geetha Mohan
This Naive Bayes Classifier tutorial presentation will introduce you to the basic concepts of Naive Bayes classifier, what is Naive Bayes and Bayes theorem, conditional probability concepts used in Bayes theorem, where is Naive Bayes classifier used, how Naive Bayes algorithm works with solved examples, advantages of Naive Bayes. By the end of this presentation, you will also implement Naive Bayes algorithm for text classification in Python. The topics covered in this Naive Bayes presentation are as follows: 1. What is Naive Bayes? 2. Naive Bayes and Machine Learning 3. Why do we need Naive Bayes? 4. Understanding Naive Bayes Classifier 5. Advantages of Naive Bayes Classifier 6. Demo - Text Classification using Naive Bayes - - - - - - - - Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer Why learn Machine Learning? Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. - - - - - - - -
Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With ...
Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With ...
Simplilearn
Generative adversarial networks ( GAN ) slides at FastCampus tutorial session.
Generative adversarial networks
Generative adversarial networks
남주 김
Houston machine learning meetup LSTM session
Long Short Term Memory
Long Short Term Memory
Yan Xu
Presentation on K-Means Clustering
Presentation on K-Means Clustering
Pabna University of Science & Technology
“Automatically learning multiple levels of representations of the underlying distribution of the data to be modelled” Deep learning algorithms have shown superior learning and classification performance. In areas such as transfer learning, speech and handwritten character recognition, face recognition among others. (I have referred many articles and experimental results provided by Stanford University)
Deep Learning - A Literature survey
Deep Learning - A Literature survey
Akshay Hegde
Talk on Optimization for Deep Learning, which gives an overview of gradient descent optimization algorithms and highlights some current research directions.
Optimization for Deep Learning
Optimization for Deep Learning
Sebastian Ruder
Algorithms and data structures in a nutshell
funwithalgorithms.pptx
funwithalgorithms.pptx
Tess Ferrandez
Debugging memory leaks, hangs, performance issues and crashses in .net applications on Windows or Linux with Windbg, dotnet dump and other tools
Debugging .NET apps
Debugging .NET apps
Tess Ferrandez
Mais conteúdo relacionado
Mais procurados
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
Recurrent Neural Networks, LSTM and GRU
Recurrent Neural Networks, LSTM and GRU
ananth
Machine Learning presentation.
Machine Learning presentation.
butest
Crop prediction using machine learning
Crop prediction using machine learning
Crop prediction using machine learning
dataalcott
This Artificial Intelligence presentation will introduce you to the AI world and give you a short glimpse about AI history. By the end of this video, you will learn what is Artificial intelligence, types of Artificial Intelligence, applications of Artificial Intelligence and future of Artificial Intelligence. The topics covered in this presentation are as follows: 1. Brief History of Artificial Intelligence 2. What is Artificial intelligence 3. Types of Artificial Intelligence 4. Applications of Artificial Intelligence 5. Future of Artificial Intelligence ----------------------------------------------------------------------------------------- Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming. Why learn Artificial Intelligence? The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills. You can gain in-depth knowledge of Artificial Intelligence by taking our Artificial Intelligence certification training course. Those who complete the course will be able to: Master the concepts of supervised and unsupervised learning Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project. Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning. Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation systems -----------------------------------------------------------------------------------------
What Is Artificial Intelligence? | Artificial Intelligence For Beginners | Wh...
What Is Artificial Intelligence? | Artificial Intelligence For Beginners | Wh...
Simplilearn
This K-Means clustering algorithm presentation will take you through the machine learning introduction, types of clustering algorithms, k-means clustering, how does K-Means clustering work and at least explains K-Means clustering by taking a real life use case. This Machine Learning algorithm tutorial video is ideal for beginners to learn how K-Means clustering work. Below topics are covered in this K-Means Clustering Algorithm presentation: 1. Types of Machine Learning? 2. What is K-Means Clustering? 3. Applications of K-Means Clustering 4. Common distance measure 5. How does K-Means Clustering work? 6. K-Means Clustering Algorithm 7. Demo: k-Means Clustering 8. Use case: Color compression - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - -
K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...
K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...
Simplilearn
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist). What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA). Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Deep learning - A Visual Introduction
Deep learning - A Visual Introduction
Lukas Masuch
This presentation about Deep Learning with Python will help you understand what is deep learning, applications of deep learning, what is a neural network, biological versus artificial neural networks, introduction to TensorFlow, activation function, cost function, how neural networks work, and what gradient descent is. Deep learning is a technology that is used to achieve machine learning through neural networks. We will also look into how neural networks can help achieve the capability of a machine to mimic human behavior. We'll also implement a neural network manually. Finally, we'll code a neural network in Python using TensorFlow. Below topics are explained in this Deep Learning with Python presentation: 1. What is Deep Learning 2. Biological versus Artificial Intelligence 3. What is a Neural Network 4. Activation function 5. Cost function 6. How do Neural Networks work 7. How do Neural Networks learn 8. Implementing the Neural Network 9. Gradient descent 10. Deep Learning platforms 11. Introduction to TensoFlow 12. Implementation in TensorFlow You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline 2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before 3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces 4. Build deep learning models in TensorFlow and interpret the results 5. Understand the language and fundamental concepts of artificial neural networks 6. Troubleshoot and improve deep learning models 7. Build your own deep learning project 8. Differentiate between machine learning, deep learning, and artificial intelligence There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals: 1. Software engineers 2. Data scientists 3. Data analysts 4. Statisticians with an interest in deep learning Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
Deep Learning With Python | Deep Learning And Neural Networks | Deep Learning...
Deep Learning With Python | Deep Learning And Neural Networks | Deep Learning...
Simplilearn
Practical Data Science. Slides of an 1 hour introductory talk about predictive modeling using Machine Learning with a focus on supervised learning.
An Introduction to Supervised Machine Learning and Pattern Classification: Th...
An Introduction to Supervised Machine Learning and Pattern Classification: Th...
Sebastian Raschka
Brief introduction on attention mechanism and its application in neural machine translation, especially in transformer, where attention was used to remove RNNs completely from NMT.
Notes on attention mechanism
Notes on attention mechanism
Khang Pham
This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work. Below topics are explained in this recurrent neural networks tutorial: 1. What is a neural network? 2. Popular neural networks? 3. Why recurrent neural network? 4. What is a recurrent neural network? 5. How does an RNN work? 6. Vanishing and exploding gradient problem 7. Long short term memory (LSTM) 8. Use case implementation of LSTM Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist. Why Deep Learning? It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results. And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: Learn more at: https://www.simplilearn.com/
Recurrent Neural Network (RNN) | RNN LSTM Tutorial | Deep Learning Course | S...
Recurrent Neural Network (RNN) | RNN LSTM Tutorial | Deep Learning Course | S...
Simplilearn
This Support Vector Machine (SVM) presentation will help you understand Support Vector Machine algorithm, a supervised machine learning algorithm which can be used for both classification and regression problems. This SVM presentation will help you learn where and when to use SVM algorithm, how does the algorithm work, what are hyperplanes and support vectors in SVM, how distance margin helps in optimizing the hyperplane, kernel functions in SVM for data transformation and advantages of SVM algorithm. At the end, we will also implement Support Vector Machine algorithm in Python to differentiate crocodiles from alligators for a given dataset. Below topics are explained in this Support Vector Machine presentation: 1. What is Machine Learning? 2. Why support vector machine? 3. What is support vector machine? 4. Understanding support vector machine 5. Advantages of support vector machine 6. Use case in Python - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - -
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Simplilearn
dddddddddddddddddddddddddddddddddddddddddddddddddddddddffffffffffffffffffffffffffffffffffffffffffffvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbcccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccojjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjppppppppppppppppppppppppppppppppppppooooooooooooooooooooooooooooooooooooooooooooojjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjpppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiikkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj
Deep learning.pptx
Deep learning.pptx
MdMahfoozAlam5
This slide first introduces the sequential pattern mining problem and also presents some required definitions in order to understand GSP algorithm. At then end there is a brief introduction of GSP algorithm and some practical constraints which it supports.
Sequential Pattern Mining and GSP
Sequential Pattern Mining and GSP
Hamidreza Mahdavipanah
Machine learning
Machine learning
Machine learning
Dr Geetha Mohan
This Naive Bayes Classifier tutorial presentation will introduce you to the basic concepts of Naive Bayes classifier, what is Naive Bayes and Bayes theorem, conditional probability concepts used in Bayes theorem, where is Naive Bayes classifier used, how Naive Bayes algorithm works with solved examples, advantages of Naive Bayes. By the end of this presentation, you will also implement Naive Bayes algorithm for text classification in Python. The topics covered in this Naive Bayes presentation are as follows: 1. What is Naive Bayes? 2. Naive Bayes and Machine Learning 3. Why do we need Naive Bayes? 4. Understanding Naive Bayes Classifier 5. Advantages of Naive Bayes Classifier 6. Demo - Text Classification using Naive Bayes - - - - - - - - Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer Why learn Machine Learning? Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. - - - - - - - -
Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With ...
Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With ...
Simplilearn
Generative adversarial networks ( GAN ) slides at FastCampus tutorial session.
Generative adversarial networks
Generative adversarial networks
남주 김
Houston machine learning meetup LSTM session
Long Short Term Memory
Long Short Term Memory
Yan Xu
Presentation on K-Means Clustering
Presentation on K-Means Clustering
Pabna University of Science & Technology
“Automatically learning multiple levels of representations of the underlying distribution of the data to be modelled” Deep learning algorithms have shown superior learning and classification performance. In areas such as transfer learning, speech and handwritten character recognition, face recognition among others. (I have referred many articles and experimental results provided by Stanford University)
Deep Learning - A Literature survey
Deep Learning - A Literature survey
Akshay Hegde
Talk on Optimization for Deep Learning, which gives an overview of gradient descent optimization algorithms and highlights some current research directions.
Optimization for Deep Learning
Optimization for Deep Learning
Sebastian Ruder
Mais procurados
(20)
Recurrent Neural Networks, LSTM and GRU
Recurrent Neural Networks, LSTM and GRU
Machine Learning presentation.
Machine Learning presentation.
Crop prediction using machine learning
Crop prediction using machine learning
What Is Artificial Intelligence? | Artificial Intelligence For Beginners | Wh...
What Is Artificial Intelligence? | Artificial Intelligence For Beginners | Wh...
K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...
K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...
Deep learning - A Visual Introduction
Deep learning - A Visual Introduction
Deep Learning With Python | Deep Learning And Neural Networks | Deep Learning...
Deep Learning With Python | Deep Learning And Neural Networks | Deep Learning...
An Introduction to Supervised Machine Learning and Pattern Classification: Th...
An Introduction to Supervised Machine Learning and Pattern Classification: Th...
Notes on attention mechanism
Notes on attention mechanism
Recurrent Neural Network (RNN) | RNN LSTM Tutorial | Deep Learning Course | S...
Recurrent Neural Network (RNN) | RNN LSTM Tutorial | Deep Learning Course | S...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Deep learning.pptx
Deep learning.pptx
Sequential Pattern Mining and GSP
Sequential Pattern Mining and GSP
Machine learning
Machine learning
Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With ...
Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With ...
Generative adversarial networks
Generative adversarial networks
Long Short Term Memory
Long Short Term Memory
Presentation on K-Means Clustering
Presentation on K-Means Clustering
Deep Learning - A Literature survey
Deep Learning - A Literature survey
Optimization for Deep Learning
Optimization for Deep Learning
Mais de Tess Ferrandez
Algorithms and data structures in a nutshell
funwithalgorithms.pptx
funwithalgorithms.pptx
Tess Ferrandez
Debugging memory leaks, hangs, performance issues and crashses in .net applications on Windows or Linux with Windbg, dotnet dump and other tools
Debugging .NET apps
Debugging .NET apps
Tess Ferrandez
Debugging performance issues, hangs, crashes and memory problems with memory dumps
CSI .net core - debugging .net applications
CSI .net core - debugging .net applications
Tess Ferrandez
slides for interactive session about debugging .net at TechEd Barcelona 2008
Debugging performance issues, memory issues and crashes in .net applications rev
Debugging performance issues, memory issues and crashes in .net applications rev
Tess Ferrandez
Debugging .net issues - Presentation for TechEd EMEA 2008
Common asp.net production issues rev
Common asp.net production issues rev
Tess Ferrandez
PDC 2008 Presentation by Rico Mariani and Vance Morrison
Perf by design
Perf by design
Tess Ferrandez
GC presentation - Maoni Stephens - PDC 2005
Fun421 stephens
Fun421 stephens
Tess Ferrandez
As a long time C# developer, I started with Python as a second language for ML purposes. Starting in Python is easy, doing engineering grade python turned out to be a lot harder, so these are 10 things I learned along the way to writing production code in Python.
C# to python
C# to python
Tess Ferrandez
Paper review of the FaceNet paper for the Machine Learning at Microsoft Youtube channel. https://www.youtube.com/watch?v=w--c0qG9MCc&t=2392s
Facenet - Paper Review
Facenet - Paper Review
Tess Ferrandez
We're in charge, as data scientist and software engineers, of software that where mistakes have a very high price. It's time to look at the impact we have, how our software will be used and how to avoid creating unfair, biased and dangerous software
AI and Ethics - We are the guardians of our future
AI and Ethics - We are the guardians of our future
Tess Ferrandez
Updated version of the Practical Guide to Deep Learning with some actual projects, and our process of working through deep learning projects - less end to end, more joining several models together
Deep learning and computer vision
Deep learning and computer vision
Tess Ferrandez
An intro to Deep Learning on Images using CNNs and Keras, along with some tips and tricks to make your models perform better
A practical guide to deep learning
A practical guide to deep learning
Tess Ferrandez
A developers guide to machine learning
A developers guide to machine learning
A developers guide to machine learning
Tess Ferrandez
Supporting slides for a presentation on how to think about UX for bots and the Microsoft Bot Framework
My bot has a personality disorder
My bot has a personality disorder
Tess Ferrandez
Slides for .NET Debugging Presentation
.Net debugging 2017
.Net debugging 2017
Tess Ferrandez
Mais de Tess Ferrandez
(15)
funwithalgorithms.pptx
funwithalgorithms.pptx
Debugging .NET apps
Debugging .NET apps
CSI .net core - debugging .net applications
CSI .net core - debugging .net applications
Debugging performance issues, memory issues and crashes in .net applications rev
Debugging performance issues, memory issues and crashes in .net applications rev
Common asp.net production issues rev
Common asp.net production issues rev
Perf by design
Perf by design
Fun421 stephens
Fun421 stephens
C# to python
C# to python
Facenet - Paper Review
Facenet - Paper Review
AI and Ethics - We are the guardians of our future
AI and Ethics - We are the guardians of our future
Deep learning and computer vision
Deep learning and computer vision
A practical guide to deep learning
A practical guide to deep learning
A developers guide to machine learning
A developers guide to machine learning
My bot has a personality disorder
My bot has a personality disorder
.Net debugging 2017
.Net debugging 2017
Último
Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
debabhi2
A Principled Technologies deployment guide Conclusion Deploying VMware Cloud Foundation 5.1 on next gen Dell PowerEdge servers brings together critical virtualization capabilities and high-performing hardware infrastructure. Relying on our hands-on experience, this deployment guide offers a comprehensive roadmap that can guide your organization through the seamless integration of advanced VMware cloud solutions with the performance and reliability of Dell PowerEdge servers. In addition to the deployment efficiency, the Cloud Foundation 5.1 and PowerEdge solution delivered strong performance while running a MySQL database workload. By leveraging VMware Cloud Foundation 5.1 and PowerEdge servers, you could help your organization embrace cloud computing with confidence, potentially unlocking a new level of agility, scalability, and efficiency in your data center operations.
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Principled Technologies
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
The Digital Insurer
writing some innovation for development and search
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
sudhanshuwaghmare1
Breathing New Life into MySQL Apps With Advanced Postgres Capabilities
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
RTylerCroy
Explore the top 10 most downloaded games on the Play Store in 2024, reflecting the latest gaming trends. As a premier game development company in India, we're committed to crafting innovative and engaging gaming experiences. Partner with us to bring your game ideas to life and captivate audiences worldwide. Visit here:- https://www.synarionit.com/game-development-company-in-india.html
Top 10 Most Downloaded Games on Play Store in 2024
Top 10 Most Downloaded Games on Play Store in 2024
SynarionITSolutions
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving. A report by Poten & Partners as part of the Hydrogen Asia 2024 Summit in Singapore. Copyright Poten & Partners 2024.
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Edi Saputra
Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
The Digital Insurer
Manulife - Insurer Innovation Award 2024
Manulife - Insurer Innovation Award 2024
Manulife - Insurer Innovation Award 2024
The Digital Insurer
This project focuses on implementing real-time object detection using Raspberry Pi and OpenCV. Real-time object detection is a critical aspect of computer vision applications, allowing systems to identify and locate objects within a live video stream instantly.
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
Khem
ICT role in 21 century education. How to ICT help in education
presentation ICT roal in 21st century education
presentation ICT roal in 21st century education
jfdjdjcjdnsjd
With more memory available, system performance of three Dell devices increased, which can translate to a better user experience Conclusion When your system has plenty of RAM to meet your needs, you can efficiently access the applications and data you need to finish projects and to-do lists without sacrificing time and focus. Our test results show that with more memory available, three Dell PCs delivered better performance and took less time to complete the Procyon Office Productivity benchmark. These advantages translate to users being able to complete workflows more quickly and multitask more easily. Whether you need the mobility of the Latitude 5440, the creative capabilities of the Precision 3470, or the high performance of the OptiPlex Tower Plus 7010, configuring your system with more RAM can help keep processes running smoothly, enabling you to do more without compromising performance.
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
Principled Technologies
How to get Oracle DBA Job as fresher.
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
Remote DBA Services
The Good, the Bad and the Governed - Why is governance a dirty word? David O'Neill, Chief Operating Officer - APIContext Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
apidays
The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
Joaquim Jorge
45-60 minute session deck from introducing Google Apps Script to developers, IT leadership, and other technical professionals.
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
wesley chun
Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
apidays
Presentation from Melissa Klemke from her talk at Product Anonymous in April 2024
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
Product Anonymous
Read about the journey the Adobe Experience Manager team has gone through in order to become and scale API-first throughout the organisation.
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
Radu Cotescu
Último
(20)
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
Top 10 Most Downloaded Games on Play Store in 2024
Top 10 Most Downloaded Games on Play Store in 2024
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Manulife - Insurer Innovation Award 2024
Manulife - Insurer Innovation Award 2024
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
presentation ICT roal in 21st century education
presentation ICT roal in 21st century education
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
Baixar agora