8: Convolutions (CNNs)

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:
  • HuggingFace Spaces Pets repository
  • Which image models are best?
  • How does a neural net really work?
• Other resources for the lesson
  • Titanic spreadsheet: see the course repository
  • Titanic data (training CSV) can be downloaded from Kaggle
• Solutions to chapter 4 questions from the book