> For the complete documentation index, see [llms.txt](https://cedrickchee.gitbook.io/knowledge/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://cedrickchee.gitbook.io/knowledge/courses/fast.ai/deep-learning-part-1-practical-deep-learning-for-coders/2019-edition.md).

# Deep Learning Part 1: 2019 Edition (v3)

## Course Materials

* [Application Announcement](http://forums.fast.ai/t/fast-ai-live-the-new-version-of-the-international-fellowship/22825): closed
* [Website](http://course.fast.ai/) (officially released in early 2019)

  The 3rd edition of course.fast.ai - coming in 2019. This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. The course is taught in Python, using the fastai library and PyTorch. You will start with step one—learning how to get a GPU server online suitable for deep learning—and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems.
* [Forum (suitable for people who have not studied deep learning before)](https://forums.fast.ai/c/part1-v3)
* [Forum for advanced questions](https://forums.fast.ai/c/part1-v3/part1-v3-adv)
* [FAQ, resources, and official course updates](https://forums.fast.ai/t/faq-resources-and-official-course-updates/27934)
* [Jupyter Notebook and Code](https://github.com/fastai/course-v3/tree/master/nbs/dl1)

## Lessons Cover

Many topics, including:

* image recognition
  * multi-label image classification
  * different kind of images
* Convolutional Neural Networks (CNNs)
  * image segmentation with U-Net
* overfitting
* embeddings
  * collaborative filtering: recommendation systems
* Natural Language Processing (NLP)
  * language model, sentiment analysis
  * text classification
* Recurrent Neural Networks (RNNs)
  * RNN architecture from scratch
* tabular/structured data
  * time-series prediction using neural network
* CNN architecture
  * back to computer vision
  * CNN in-depth and ResNets from scratch