Gain insights into basic and intermediate deep learning concepts, including CNNs, RNNs, GANs, and transformers. Delve into fundamental architectures to enhance your machine learning model training skills.

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

Variational autoencoders (VAE) are fascinating models. Despite their complexity, they have been proven to work very well on tasks like compression, data reconstruction, and generation. Over the last years, the vanilla architecture we described has been used as a “framework” on top of which many new models and ideas have been built. That is why it is critical to understand them correctly,  especially for those who are interested in unsupervised and generation learning.

Take a quiz on generative learning and variational autoencoders and their training process.

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

Quiz Yourself on Autoencoders