Deep Learning Part 2: 2018 Edition
Course Materials
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.
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