Today we finish off our study of collaborative filtering by looking closely at embeddings—a critical building block of many deep learning algorithms. Then we’ll dive into convolutional neural networks (CNNs) and see how they really work. We’ve used plenty of CNNs through this course, but we haven’t peeked inside them to see what’s really going on in there. As well as learning about their most fundamental building block, the convolution, we’ll also look at pooling, dropout, and more.
Video
This lesson is based partly on chapter 13 of the book.
Resources
- Notebooks for this lesson
- Collaborative Filtering Deep Dive
- Spreadsheets for this lesson
- Collaborative filterings and embeddings
- Convolutions
- Other resources for the lesson
- Please add any questions you want Jeremy to answer to the AMA thread – and upvote any there you’re interested in
- Special extra: Data ethics lesson
- Solutions to chapter 8 questionnaire from the book
