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

• Jupyter Notebook and Code

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