Deep Learning Specialization

This repository contains my personal notes and Jupyter notebooks on Deep Learning Specialization course at the University Haute-Alsace.

I'm enjoying every little bit of the course, hope you enjoy my notes too.

This course contains seven chapters. the seven chapters titles are:

• MLP
• Autoencoder & Variational Autoencoder
• GAN
• Explainability
• Self-attention / Transformers
• Self-supervised Learning
• Object Detection

About the course

Deep Learning is one of the most highly sought-after tech skills.

In this course, I will learn advanced concepts of neural networks and deep learning, and understand the recent architectures and their use cases. I will also be implementing a multilayer perceptron network from scratch in Python.

AI is transforming multiple industries. After finishing this specialization course, I will likely find creative ways to apply it to my work.

References

1. Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep learning". MIT press, 2016.
2. Roberts, Daniel A., Sho Yaida, and Boris Hanin. "The Principles of Deep Learning Theory." Cambridge University Press, 2022.
3. François Chollet. "Deep Learning with Python". Manning Publications Company, 2017

Acknowledgements

I would like to express my deepest appreciation to my professors Maxime Devanne, Germain Forestier and Jonathan Weber for their generously provided knowledge, invaluable patience, feedback and expertise.

Corrections ?

If you find any issues in these code examples, feel free to submit an Issue or Pull Request. I appreciate your input!

Questions ?

Reach out to @yassine_rd_ on Twitter or feel free to contact contact yassine.rodani@gmail.com. :)