Deep Learning Part 2: 2018 Edition (v2)
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
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