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

## Types of graph tasks: graph and node classification

We discussed a bit about the input representation. But what about the target (output)? The most basic tasks in graph neural networks are:


*   **Graph classification**: We have a lot of graphs and we would like to find a single label for each individual graph (similar to image classification). This task is casted as a standard supervised problem. In graphs, you will see the term **inductive learning** for this task.

*   **Node classification**: Usually, in this type of task, we have a huge graph (>5000 nodes) and we try to find a label for the nodes (similar to image segmentation). Importantly, we have **very few labeled nodes** to train the model (for instance <5%). The aim is to predict the missing labels for all the other nodes in the graph. That is why this task is formulated as a semi-supervised learning task or **transductive learning** equivalently. It is called semi-supervised because even though the nodes do **not** have labels, we feed the graph (with all the nodes) in the neural network and formulate a supervised loss term for the labeled nodes only.

# Types of graph tasks: graph and node classification

We discussed a bit about the input representation. But what about the target (output)? The most basic tasks in graph neural networks are:


*   **Graph classification**: We have a lot of graphs and we would like to find a single label for each individual graph (similar to image classification). This task is casted as a standard supervised problem. In graphs, you will see the term **inductive learning** for this task.

*   **Node classification**: Usually, in this type of task, we have a huge graph (>5000 nodes) and we try to find a label for the nodes (similar to image segmentation). Importantly, we have **very few labeled nodes** to train the model (for instance <5%). The aim is to predict the missing labels for all the other nodes in the graph. That is why this task is formulated as a semi-supervised learning task or **transductive learning** equivalently. It is called semi-supervised because even though the nodes do **not** have labels, we feed the graph (with all the nodes) in the neural network and formulate a supervised loss term for the labeled nodes only.

Discover how Graph Convolutional Networks are conceived: a deep dive into the mathematics behind them.

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

Graph Convolutional Networks

Types of graph tasks: graph and node classification