Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Getting started with ai for free devopsdays rdu

10 views

Published on

Curious about AI? Find out how you can get started today! We’ll walk through several tools that are open source and commonly used in data science. I’ll also provide information about the algorithms being used and we’ll walk through a few different use-cases so you’ll better understand them.

Published in: Technology
  • Be the first to comment

  • Be the first to like this

Getting started with ai for free devopsdays rdu

  1. 1. 1 GETTING STARTED WITH AI FOR FREE A Primer Dan Barker Chief Architect - Archer @barkerd427
  2. 2. 2 Why AI? @barkerd427
  3. 3. 3 Leverage what you have What you have: ▪ User data ▪ System data ▪ Free data! What you solve: ▪ Fraud ▪ Disk space ▪ Pattern Discovery @barkerd427
  4. 4. 4 Free data and research!!! ▪ https://toolbox.google.com/datasetsearch ▪ https://www.kaggle.com/ ▪ https://scholar.google.com/ ▪ https://arxiv.org/ @barkerd427
  5. 5. 5 Jupyter Notebooks and Google Colab Data Science for everyone @barkerd427
  6. 6. 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. 7. 7 Go to Google Colab: ai.danbarker.codes
  8. 8. 8 What is AI? @barkerd427
  9. 9. 9 AI is like “Digital” ▪ Natural Language Processing ▪ Machine Learning ▪ Deep Learning ▪ Vision ▪ Robotics @barkerd427
  10. 10. 10 Natural Language Processing - NLP @barkerd427
  11. 11. 11 What is NLP? ▪ Natural Language Understanding − Sentiment Analysis ▪ Natural Language Generation @barkerd427
  12. 12. 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. 13. 13 Just starting? ▪ Python − TextBlob − Textacy @barkerd427
  14. 14. 14 Let’s look at Textacy and TextBlob
  15. 15. 15 Machine Learning @barkerd427
  16. 16. 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. 17. 17 ML Tools ▪ numpy ▪ pandas ▪ matplotlib ▪ SciPy ▪ scikit-learn ▪ PyTorch @barkerd427
  18. 18. 18 Pandas, SciPy, and Boston Housing Data
  19. 19. 19 Deep Learning @barkerd427
  20. 20. 20 What is Deep Learning? ▪ Type of Machine Learning ▪ Hierarchical feature learning ▪ Deep Neural Networks ▪ Convolutional Neural Networks ▪ Generative Adversarial Networks @barkerd427
  21. 21. 21 Deep Learning Tools ▪ TensorFlow ▪ PyTorch ▪ Keras ▪ Caffe ▪ Microsoft Cognitive Toolkit ▪ MXNet ▪ Chainer @barkerd427
  22. 22. 22 The Bias @barkerd427
  23. 23. 23 @barkerd427
  24. 24. 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. 25. 25 Complex Systems Theory ▪ we don’t actually understand cognitive biases ▪ we don’t accurately attribute success/failure ▪ move forward, but mindfully! @barkerd427
  26. 26. 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. 27. 27 Dan Barker dan@danbarker.codes danbarker.codes dan.barker@rsa.com rsa.com @barkerd427

×