Course notes

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Here are the course notes for every lesson. Many thanks to Brad Kenstler for his hard work on these! For the lessons that go with these notes, please see

  • Lesson 1 Notes - Getting started with deep learning tools for computer vision
  • Lesson 2 Notes - The basic algorithms underlying deep learning
  • Lesson 3 Notes - Review of the components of a CNN; avoiding over-fitting and under-fitting
  • Lesson 4 Notes - Convolutions and SGD gradient tutorials; State farm: learning rate selection, data augmentation tuning, pseudo-labeling and knowledge distillation; Intro to collaborative filtering
  • Lesson 5 Notes - Adding batchnorm to VGG; visualizing latent factors; functional API; NLP and word embeddings; Multi-size CNNs; RNN introduction
  • Lesson 6 Notes - MixIterator for pseudo-labeling and combining validation/training sets; Embedding Excel examples; RNNs (creating layers by hand in keras; sequence and stateful RNNs; simple RNN in Theano)
  • Lesson 7 Notes - CNN architectures: resnet, inception, fully convolutional net, multi input and multi output nets; localization with bounding box models and heatmaps; using larger inputs to CNNs; building a simple RNN in pure python; Gated recurrent units (GRUs), and how to build a GRU RNN in theano