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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:
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
- Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep learning". MIT press, 2016.
- Roberts, Daniel A., Sho Yaida, and Boris Hanin. "The Principles of Deep Learning Theory." Cambridge University Press, 2022.
- 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 on Twitter or feel free to contact contact . :)
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