# Deep Learning Part 2: 2018 Edition (v2)

## Course Materials

* [Blog post](http://www.fast.ai/2018/05/07/part2-launch/)
*   [Website](http://course.fast.ai/part2.html)

    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](http://www.fast.ai/2018/04/30/dawnbench-fastai/), fastai and PyTorch.
* [Wiki](http://forums.fast.ai/c/part2-v2)
* [Jupyter Notebook and Code](https://github.com/fastai/fastai/tree/master/courses/dl2)

## 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
