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

If you’re feeling a little lost after learning about GANs, you are not to blame. It is admittedly not an easy model to understand. The main difficulty arises from the way we train them. This is because GANs are still a topic of active research and have not been fully developed.

Hopefully, after exploring the code in the Jupyter notebook and executing a few examples, you have managed to understand everything better. If that is true, you shouldn’t face any problems in the quiz given below. 

Find out which aspect of GANs you might need to study again with a quiz.

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 GANs