Topics to Look For

  • Convolution (mathematical operation)
  • Padding
  • Stride
  • Convolutional Layers
  • Pooling Layers
  • ResNet
  • Transfer Learning
  • Data Augmentation


  • Convolutional Neural Networks:
    • Week 1: All videos (11 videos)
    • Week 2: First four videos in “Case Studies”; all videos in “Practical Advice for Using ConvNets”


  • Convolutional Neural Networks is a one-page (it’s a long page) introduction to convolutional networks written for CS231n, a course at Stanford. It somewhat shorter than the following resources (could be a pro or a con). It contains a nice animation halfway down the page showing how a convolution layer is applied across an image.
  • Convolutional Neural Networks from Dive into Deep Learning covers convolutional networks with lots of examples, diagrams, and embedded code (including PyTorch examples).
  • Chapter 6 from Neural Networks and Deep Learning introduces convolutional networks by working through an example of classifying handwritten digits.
  • Convolutional Networks from the Deep Learning book, as usual for this resource, is fairly math-heavy and quite detailed. I recommend using it as a reference in which to look up specific topics for this week to gain a different perspective and possibly a few more details.