# Deep learning

- Page: https://shared.btbytes.com/p/deep-learning
- Date: 2023-01-23
- Tags: deep-learning

## Books


- [Mathematical Engineering of Deep Learning](https://deeplearningmath.org/) (online book). (Liquet et al. 2024)
- [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python) by [François Chollet](https://en.wikipedia.org/wiki/François_Chollet) (Google; Keras, TensorFlow, ARC Challenge)
- [The little book of deep learning](https://fleuret.org/public/lbdl.pdf) [pdf] by François Fleuret -  professor of computer science at the University of Geneva, Switzerland; Apr 2023. #phoneformatted. See also [100-page-book](https://shared.btbytes.com/p/100-page-book)
- [Book | Alfredo Canziani](https://atcold.github.io/book.html) (in progress...)
- [udlbook/Understanding Deep Learning](https://udlbook.github.io/udlbook/) by Simon J.D. Prince. Has 68 Jupyter Notebooks, which can be downloaded from [github](https://github.com/udlbook/udlbook). Plots are drawn with -- <q>Inkscape with the texText plugin to do the equations. Free and highly recommended. I draw the axes and ticks by hand because it looks nicer than anything from PyPlot etc.</q> <date class="wlogdate" id="wlog-2023-11-15">2023-11-15</date> <a href="#wlog-2023-11-15">¶</a>
- [Deep Learning: Foundations and Concepts](https://issuu.com/cmb321/docs/deep_learning_ebook) by Chris Bishop of PRML book fame.
- [[2106.10165] The Principles of Deep Learning Theory](https://arxiv.org/abs/2106.10165)

## Papers

- [Browse the State-of-the-Art in Machine Learning | Papers With Code](https://paperswithcode.com/sota)


## Articles

- [From Deep to Long Learning? · Hazy Research](https://hazyresearch.stanford.edu/blog/2023-03-27-long-learning)
- [A Completely Non-Technical Explanation of AI and Deep Learning - Parand](https://www.parand.com/a-completely-non-technical-explanation-of-ai.html)

## Learning Paths

- [Learning From Data - Online Course (MOOC)](https://work.caltech.edu/telecourse)
- [MIT Deep Learning 6.S191](http://introtodeeplearning.com/)
- [AI for Beginners](https://microsoft.github.io/AI-For-Beginners/) from [Microsoft](https://shared.btbytes.com/p/microsoft)
- [Data Science for Beginners](https://microsoft.github.io/Data-Science-For-Beginners/#/) from [Microsoft](https://shared.btbytes.com/p/microsoft)
- [Machine Learning for Beginners](https://microsoft.github.io/ML-For-Beginners/#/) from [Microsoft](https://shared.btbytes.com/p/microsoft)
- [Stanford University CS231n: Deep Learning for Computer Vision](http://cs231n.stanford.edu/)
- [Stanford CS229: Machine Learning I Spring 2022 - YouTube](https://www.youtube.com/playlist?app=desktop&list=PLoROMvodv4rNyWOpJg_Yh4NSqI4Z4vOYy)
- [UNIGE 14x050 – Deep Learning](https://fleuret.org/dlc/) course by [François Fleuret](https://fleuret.org/francois/), who also authored "The Little book of deep learning"
- [Practical Deep Learning for Coders](https://course.fast.ai/) from fast.ai, the book - [Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527)
	- [fast.ai – Alternatives to a Degree to Prove Yourself in Deep Learning](https://www.fast.ai/posts/2017-04-06-alternatives.html)


## Courses

[Sargur Shrihari at Uni Buffalo](https://cedar.buffalo.edu/~srihari/)

- [Deep Learning](https://cedar.buffalo.edu/~srihari/CSE676/) prerequisites
	- [Introduction to Machine Learning: Course Materials](https://cedar.buffalo.edu/~srihari/CSE574/)
	- [Probabilistic Graphical Models: Course Slides](https://cedar.buffalo.edu/~srihari/CSE674/)


## Videos

- [Practical Deep Learning for Coders - YouTube](https://www.youtube.com/playlist?list=PLfYUBJiXbdtSvpQjSnJJ_PmDQB_VyT5iU) by [Jeremy Howard](https://x.com/jeremyphoward)
- after the above watch this this from 2019 [Deep Learning from the Foundations - YouTube](https://www.youtube.com/watch?v=4u8FxNEDUeg&list=PLfYUBJiXbdtTIdtE1U8qgyxo4Jy2Y91uj)
- [Endless Experimentation - building models in the real world](https://x.com/svpino/status/1829576853859778592) by Nikolas Laskaris and Douglas Blank from CometAI at MIT [intro to deep learning](http://introtodeeplearning.com/)


## Blogs

- [kipply's blog](https://kipp.ly/blog/)
- [Lilian Weng's blog archive](https://lilianweng.github.io/archives/)


## People

- [Demis Hassabis - Wikipedia](https://en.wikipedia.org/wiki/Demis_Hassabis) CEO of deepmind.
- [Alec Radford](https://x.com/jxmnop/status/1725949517940294055), researcher at OpenAI; [google scholar page](https://scholar.google.com/citations?user=dOad5HoAAAAJ&hl=en)

## References

- Liquet, Moka, Nazarathy (2024). Mathematical Engineering of Deep Learning.