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Artificial Intelligence.docx

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Artificial Intelligence.docx

  1. 1. i NEURAL NETWORK IN ARTIFICIAL INTELLIGENCE Table of Contents 1. Introduction:.....................................................................................................................1 2. Artificial Intelligence:.......................................................................................................1 2.1 Introduction to Artificial Intelligence .....................................................................1 2.2 How Artificial Intelligence Works?.........................................................................1 2.3 Advantages.................................................................................................................1 3. Machine Learning.............................................................................................................2 3.1 Machine Learning Methods .....................................................................................2 3.1.1 Supervised Learning..........................................................................................2 3.1.2 Unsupervised Learning .....................................................................................2 3.1.3 Semi-supervised Learning.................................................................................2 4. Neural Network.................................................................................................................3 4.1 Layering Structure....................................................................................................3 4.2 Applications of Neural Network ..............................................................................3 4.3 Explanation................................................................................................................4 4.3.1 Back propagation...............................................................................................4 4.4 How Neural Network actually works? ....................................................................5
  2. 2. ii ....................................................................................................................................................5 5. References..........................................................................................................................6
  3. 3. 1 1. Introduction: Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning. 2. Artificial Intelligence: The intelligence proved by machines is known as Artificial Intelligence. Artificial Intelligence has grown to be very popular in today’s world. It is the recreation of natural intelligence in machines that are programmed to learn and impressionist the actions of humans. These machines are able to learn with experience and perform human-like tasks. As technologies such as AI continue to grow, they will have a great impact on our quality of life. It’s but natural that everyone today wants to connect with AI technology somehow, may it be as an end-user or following a career in Artificial Intelligence.[1] 2.1 Introduction to Artificial Intelligence The short answer to What is Artificial Intelligence is that it depends on who you ask. A layman with a brief understanding of technology would link it to robots. They’d say Artificial Intelligence is a terminator like-figure that can act and think on its own. If you ask about artificial intelligence to an AI researcher, (s)he would say that it’s a set of algorithms that can produce results without having to be explicitly instructed to do so.[1] 2.2 How Artificial Intelligence Works? To understand How Artificial Intelligence actually works, one needs to deep dive into the various sub-domains of Artificial Intelligence and understand how those domains could be applied to the various fields of the industry.  Machine Learning: ML teaches a machine how to make inferences and decisions based on past experience. It identifies patterns, analyses past data to infer the meaning of these data points to reach a possible conclusion without having to involve human experience. This automation to reach conclusions by evaluating data, saves a human time for businesses and helps them make a better decision.  Neural Network: Neural Networks work on the similar principles as of Human Neural cells. They are a series of algorithms that captures the relationship between various underlying variables and processes the data as a human brain does. 2.3 Advantages  Reduction in human error  Available 24x7  Faster and more accurate decisions  Improves security  Digital assistance
  4. 4. 2  Medical applications  Helps in repetitive work 3. Machine Learning Machine learning is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly. 3.1 Machine Learning Methods Machine learning algorithms are often categorized as supervised or unsupervised. 3.1.1 Supervised Learning It can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly. 3.1.2 Unsupervised Learning It is used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data. 3.1.3 Semi-supervised Learning It falls somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.
  5. 5. 3 4. Neural Network ‘Neural’ word derived from “neurons” and ‘Network’ means “combining”. So Neural Network means combining neurons together. Input data Meaning Neural Network implementation Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning. Artificial neural networks (ANN) have been developed as generalizations of mathematical models of biological nervous systems. An Artificial Neural Network is a network of collections of very simple processors ("Neurons") 4.1 Layering Structure A neural network has three layers in its structure.  First layer is input layer which is directly interact with external worlds  Second layer is of hidden unit where computation is done according to function provided  Last layer is output layer from where we get output.[4] Layer 2 (Hidden layer) Layer 1 Layer 3 (Input layer) (Output layer) 4.2 Applications of Neural Network  Facial Recognition  Real-Time translation
  6. 6. 4 4.3 Explanation Neural Network form the base of deep learning, a subfield of machine learning where algorithms are inspired by the structure of human brain. Neural Networks take in data and train themselves to recognize the patterns in this data and then predict the outputs. 4.3.1 Back propagation Backpropagation is the essence of neural network training. It is the method of fine-tuning the weights of a neural network based on the error rate obtained in the previous epoch (i.e., iteration). Proper tuning of the weights allows you to reduce error rates and make the model reliable by increasing its generalization. The back propagation (BP) neural network algorithm is a multi-layer feedforward network trained according to error back propagation algorithm and is one of the most widely applied neural network models. BP network can be used to learn and store a great deal of mapping relations of input-output model, and no need to disclose in advance the mathematical equation that describes these mapping relations. Its learning rule is to adopt the steepest descent method in which the back propagation is used to regulate the weight value and threshold value of the network to achieve the minimum error sum of square.[2]
  7. 7. 5 4.4 How Neural Network actually works? b1 W1 Wa b2 x1 Wb W2 Wc Wx b3 W3 x2 Wy Wz V1=f (WaU1+WxU2+b1) V2=f (WbU1+WyU2+b2) V3=f (WcU1+WzU2+b3) U1 U2 V3 V2 V1
  8. 8. 6 5. References [1] https://www.mygreatlearning.com/blog/what-is-artificial-intelligence/ [2] https://www.ibm.com/cloud/learn/neural-networks [3] https://www.expert.ai/blog/machine-learning-definition/ [4] Kumar, K. and Thakur, G.S.M., 2012. Advanced applications of neural networks and artificial intelligence: A review. International journal of information technology and computer science, 4(6), p.57. [5] Li, J., Cheng, J.H., Shi, J.Y. and Huang, F., 2012. Brief introduction of back propagation (BP) neural network algorithm and its improvement. In Advances in computer science and information engineering (pp. 553-558). Springer, Berlin, Heidelberg.

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