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

It is true that slowly Recurrent Neural Networks and LSTMs have been outperformed by other state-of-the-art models, at least in the NLP area. That doesn’t mean, though, that they are not relevant for sequence processing. Every deep learning engineer and data scientist should have a solid grasp of RNNs and LSTMs and how they can be applied to various problems.



Answer some questions on Recurrent Neural Networks and LSTMS.

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 RNNs