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