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

## What is a neuron? 

In the world of Neural Networks (NN), the basic building block is the neuron. NNs are nothing more than a collection of neurons organized in layers, with information passing from one layer to the other. So to understand NN, we first need to understand the neuron: the basic computing unit.

# What is a neuron? 

In the world of Neural Networks (NN), the basic building block is the neuron. NNs are nothing more than a collection of neurons organized in layers, with information passing from one layer to the other. So to understand NN, we first need to understand the neuron: the basic computing unit.

Understand the neural network, how it is formulated, and why it works.

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

Neural Networks

What is a neuron?