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Deep Learning Part 2: 2018 Edition (v2)

Course Materials

  • Blog post
  • Website
    You'll learn the latest developments in deep learning, how to read and implement new academic papers, and how to solve challenging end-to-end problems such as natural language translation. You'll develop a deep understanding of neural network foundations, the most important recent advances in the fields, and how to implement them in the world's fastest deep learning libraries, fastai and PyTorch.
  • Wiki

Lessons Cover

Many topics, including:
  • multi-object detection with SSD and YOLOv3
  • how to read academic papers
  • customizing a pre-trained model with a custom head
  • more complex data augmentation (for coordinate variables, per-pixel classification, etc)
  • NLP transfer learning
  • handling very large (billion+ token) text corpuses with the new fastai.text library
  • running and interpreting ablation studies
  • state of the art NLP classification
  • multi-modal learning
  • multi-task learning
  • bidirectional LSTM with attention for seq2seq
  • neural translation
  • customizing resnet architectures
  • GANs, WGAN, and CycleGAN
  • data ethics
  • super resolution
  • image segmentation with U-Net