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1
GETTING STARTED
WITH AI FOR FREE
A Primer
Dan Barker
Chief Architect - Archer
@barkerd427
2
Why AI?
@barkerd427
3
Leverage what you have
What you have:
▪ User data
▪ System data
▪ Free data!
What you solve:
▪ Fraud
▪ Disk space
▪ Pattern Discovery
@barkerd427
4
Free data and research!!!
▪ https://toolbox.google.com/datasetsearch
▪ https://www.kaggle.com/
▪ https://scholar.google.com/
▪ https://arxiv.org/
@barkerd427
5
Jupyter Notebooks and
Google Colab
Data Science for everyone
@barkerd427
6
How to learn for free
▪ Jupyter Notebook
− Runs Locally
− Uses your local resources
▪ Google Colab
− Uses cloud resources
− Has GPUs and TPUs
− Can use local runtime
@barkerd427
7
Go to Google Colab:
ai.danbarker.codes
8
What is AI?
@barkerd427
9
AI is like “Digital”
▪ Natural Language Processing
▪ Machine Learning
▪ Deep Learning
▪ Vision
▪ Robotics
@barkerd427
10
Natural Language
Processing - NLP
@barkerd427
11
What is NLP?
▪ Natural Language Understanding
− Sentiment Analysis
▪ Natural Language Generation
@barkerd427
12
NLP Tools
▪ Python
− Natural Language Toolkit (NLTK)
− TextBlob
− SpaCy
− Textacy
− PyTorch-NLP
▪ Node
− Retext
− Compromise
− Natural
− Nlp.js
@barkerd427 https://opensource.com/article/19/3/natural-language-processing-tools
13
Just starting?
▪ Python
− TextBlob
− Textacy
@barkerd427
14
Let’s look at Textacy and TextBlob
15
Machine Learning
@barkerd427
16
What is Machine Learning?
▪ Supervised learning
− predictive
− labeled data
− regression and classification
− Nearest Neighbor, Naive Bayes, Decision Trees, Linear
Regression
▪ Unsupervised Learning
− descriptive
− clustering and association
− k-means, Agglomerative Hierarchical Clustering
@barkerd427
17
ML Tools
▪ numpy
▪ pandas
▪ matplotlib
▪ SciPy
▪ scikit-learn
▪ PyTorch
@barkerd427
18
Pandas, SciPy, and Boston
Housing Data
19
Deep Learning
@barkerd427
20
What is Deep Learning?
▪ Type of Machine Learning
▪ Hierarchical feature learning
▪ Deep Neural Networks
▪ Convolutional Neural Networks
▪ Generative Adversarial Networks
@barkerd427
21
Deep Learning Tools
▪ TensorFlow
▪ PyTorch
▪ Keras
▪ Caffe
▪ Microsoft Cognitive Toolkit
▪ MXNet
▪ Chainer
@barkerd427
22
The Bias
@barkerd427
23 @barkerd427
24
It’s a brand new world
▪ cognitive biases have been studied for years
− we still can’t avoid them
▪ algorithmic biases are barely understood
− we don’t know what we don’t know
− first covered in 1976
− not again for 30 years
− and nothing significant until recently
@barkerd427
25
Complex Systems Theory
▪ we don’t actually understand
cognitive biases
▪ we don’t accurately attribute
success/failure
▪ move forward, but mindfully!
@barkerd427
26
Resources
▪ https://www.kdnuggets.com/
▪ https://www.coursera.org/learn/machine-learning
▪ https://towardsdatascience.com/
▪ https://opensource.com/
▪ https://colab.research.google.com
▪ https://www.kaggle.com/
▪ https://toolbox.google.com/datasetsearch
▪ https://scholar.google.com/
▪ https://arxiv.org/
▪ https://weaponsofmathdestructionbook.com/
@barkerd427
27
Dan Barker
dan@danbarker.codes
danbarker.codes
dan.barker@rsa.com
rsa.com
@barkerd427
Getting started with ai for free devopsdays rdu

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Getting started with ai for free devopsdays rdu