The self-contained Deep learning course launched by experienced professional engineers and researchers of the AI Summer team.

docker.tar.gz

Pytorch

GANS

This course is an accumulation of well-grounded knowledge and experience in deep learning. It provides you with the basic concepts you need in order to start working with and training various machine learning models. 

You will cover both basic and intermediate concepts including but not limited to: convolutional neural networks, recurrent neural networks, generative adversarial networks as well as transformers.

After completing this course, you will have a comprehensive understanding of the fundamental architectural components of deep learning. Whether you’re a data and computer scientist, computer and big data engineer, solution architect, or software engineer, you will benefit from this course.

Introduction to Deep Learning & Neural Networks


In this lesson, we will explore graph neural networks and graph convolutions. **Graphs are a super general representation of data with intrinsic structure**. 

The most intuitive transition to graphs is by starting from images. 

Why?

Because images are **highly-structured** data. Their components (pixels) are arranged in a meaningful way. If you change the way pixels are structured, the image loses its meaning. Additionally, images have a very strong notion of **locality**.

Understand the basic concepts of graphs, how they can be represented, and their formulation.

Learn Deep Learning

Neural Networks

Training Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks

Autoencoders

Generative Adversarial Networks

Attention and Transformers

Graph Neural Networks

Conclusion

Final Quiz

Basics of Graphs