Getting started with ai for free devopsdays rdu
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The document provides an introduction to leveraging AI for free, focusing on available resources such as user and system data, as well as various open datasets. It covers key concepts in AI, including natural language processing, machine learning, and deep learning, along with practical tools and platforms like Jupyter Notebooks and Google Colab. The importance of understanding biases in AI systems is also emphasized, along with a list of educational resources for further learning.
Getting started with ai for free devopsdays rdu
- 1. 1 GETTING STARTED WITH AI FOR FREE A Primer Dan Barker Chief Architect - Archer @barkerd427
- 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 Free data and research!!! ▪ https://toolbox.google.com/datasetsearch ▪ https://www.kaggle.com/ ▪ https://scholar.google.com/ ▪ https://arxiv.org/ @barkerd427
- 5. 5 Jupyter Notebooks and Google Colab Data Science for everyone @barkerd427
- 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 Go to Google Colab: ai.danbarker.codes
- 9. 9 AI is like “Digital” ▪ Natural Language Processing ▪ Machine Learning ▪ Deep Learning ▪ Vision ▪ Robotics @barkerd427
- 10. 10 Natural Language Processing - NLP @barkerd427
- 11. 11 What is NLP? ▪ Natural Language Understanding − Sentiment Analysis ▪ Natural Language Generation @barkerd427
- 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 Just starting? ▪ Python − TextBlob − Textacy @barkerd427
- 14. 14 Let’s look at Textacy and TextBlob
- 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 ML Tools ▪ numpy ▪ pandas ▪ matplotlib ▪ SciPy ▪ scikit-learn ▪ PyTorch @barkerd427
- 18. 18 Pandas, SciPy, and Boston Housing Data
- 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 Deep Learning Tools ▪ TensorFlow ▪ PyTorch ▪ Keras ▪ Caffe ▪ Microsoft Cognitive Toolkit ▪ MXNet ▪ Chainer @barkerd427
- 23. 23 @barkerd427
- 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 Complex Systems Theory ▪ we don’t actually understand cognitive biases ▪ we don’t accurately attribute success/failure ▪ move forward, but mindfully! @barkerd427
- 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