How to Use Jupyter Notebooks for Machine Learning and AI Tasks

How to Use Jupyter Notebooks for Machine Learning and AI Tasks

Pinecone via YouTube Direct link

Intro

1 of 27

1 of 27

Intro

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Classroom Contents

How to Use Jupyter Notebooks for Machine Learning and AI Tasks

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  1. 1 Intro
  2. 2 What are Jupyter Notebooks?
  3. 3 Finding the Getting Started guide
  4. 4 The Jupyter Notebook file format. Integration with GitHub
  5. 5 What are cells?
  6. 6 Why you need to understand the security implications of using Notebooks
  7. 7 Why are Notebooks so popular?
  8. 8 My experience with Notebooks as an application/infrastructure developer
  9. 9 The semantic similarity search example Notebook we’ll be using
  10. 10 What Notebooks are ideal for - which use cases
  11. 11 How the Google Colab badge/button works
  12. 12 Why do we need Google Colab at all?
  13. 13 The initial Gotchas preventing smooth loading of a Notebook in Colab
  14. 14 How code cells work
  15. 15 What do ! exclamation points mean in front of commands in cells?
  16. 16 How scope works in Jupyter Notebooks
  17. 17 Different running modes for Jupyter Notebooks
  18. 18 How you can use Notebooks to help you test things
  19. 19 How to securely work with secrets like API keys
  20. 20 What are secrets and why are they important?
  21. 21 Loading your Pinecone API key securely
  22. 22 Working with Pinecone Indexes
  23. 23 The original Kaggle challenge dataset we’re using in this Notebook
  24. 24 How the download data function works
  25. 25 Upserting vectors to Pinecone’s vector database
  26. 26 How to query the Pinecone database via semantic search
  27. 27 Evaluating the results we get back

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