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

This was  a long chapter with lots of lessons. The reason is simple. Attention-based models and Transformers are the hottest architectures at the moment as they have managed to outperform LSTMs in NLP problems and are slowly getting there in computer vision as well. That is why it is vital that you understand them fully. That doesn’t mean that this is easy. The trick is that all the subcomponents borrow ideas and techniques from many other architectures.

Hopefully, though, you have accomplished your goal and now have a strong understanding of  Transformers. You can take the following quiz to clarify any misconceptions.




Take a multiple-choice quiz on attention and transformers.

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 Transformers