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