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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier
| Date | Stars |
|---|---|
| 2026-07-24 | 1810 |
| 2026-07-25 | 1810 |
| 2026-07-28 | 1810 |
| 2026-07-30 | 1810 |
| 2026-07-31 | 1814 |
| 2026-08-06 | 1815 |
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# [Awesome Deep Learning Resources](https://github.com/guillaume-chevalier/Awesome-Deep-Learning-Resources) [](https://github.com/sindresorhus/awesome) This is a rough list of my favorite deep learning resources. It has been useful to me for learning how to do deep learning, I use it for revisiting topics or for reference. I ([Guillaume Chevalier](https://github.com/guillaume-chevalier)) have built this list and got through all of the content listed here, carefully. ## Contents - [Trends](#trends) - [Online classes](#online-classes) - [Books](#books) - [Posts and Articles](#posts-and-articles) - [Practical resources](#practical-resources) - [Librairies and Implementations](#librairies-and-implementations) - [Some Datasets](#some-datasets) - [Other Math Theory](#other-math-theory) - [Gradient Descent Algorithms and optimization](#gradient-descent-algorithms-and-optimization) - [Complex Numbers & Digital Signal Processing](#complex-numbers-and-digital-signal-processing) - [Papers](#papers) - [Recurrent Neural Networks](#recurrent-neural-networks) - [Convolutional Neural Networks](#convolutional-neural-networks) - [Attention Mechanisms](#attention-mechanisms) - [Other](#other) - [YouTube and Videos](#youtube) - [Misc. Hubs and Links](#misc-hubs-and-links) - [License](#license) <a name="trends" /> ## Trends Here are the all-time [Google Trends](https://www.google.ca/trends/explore?date=all&q=machine%20learning,deep%20learning,data%20science,computer%20programming), from 2004 up to now, September 2017: <p align="center"> <img src="google_trends.png" width="792" height="424" /> </p> You might also want to look at Andrej Karpathy's [new post](https://medium.com/@karpathy/a-peek-at-trends-in-machine-learning-ab8a1085a106) about trends in Machine Learning research. I believe that Deep learning is the key to make computers think more like humans, and has a lot of potential. Some hard automation tasks can be solved easily with that while this was impossible to achieve earlier with classical algorithms. Moore's Law about exponential progress rates in computer science hardware is now more affecting GPUs than CPUs because of physical limits on how tiny an atomic transistor can be. We are shifting toward parallel architectures [[read more](https://www.quora.com/Does-Moores-law-apply-to-GPUs-Or-only-CPUs)]. Deep learning exploits parallel architectures as such under the hood by using GPUs. On top of that, deep learning algorithms may use Quantum Computing and apply to machine-brain interfaces in the future. I find that the key of intelligence and cognition is a very interesting subject to explore and is not yet well understood. Those technologies are promising. <a name="online-classes" /> ## Online Classes - **[DL&RNN Course](https://www.dl-rnn-course.neuraxio.com/start?utm_source=github_awesome) - I created this richely dense course on Deep Learning and Recurrent Neural Networks.** - [Machine Learning by Andrew Ng on Coursera](https://www.coursera.org/learn/machine-learning) - Renown entry-level online class with [certificate](https://www.coursera.org/account/accomplishments/verify/DXPXHYFNGKG3). Taught by: Andrew Ng, Associate Professor, Stanford University; Chief Scientist, Baidu; Chairman and Co-founder, Coursera. - [Deep Learning Specialization by Andrew Ng on Coursera](https://www.coursera.org/specializations/deep-learning) - New series of 5 Deep Learning courses by Andrew Ng, now with Python rather than Matlab/Octave, and which leads to a [specialization certificate](https://www.coursera.org/account/accomplishments/specialization/U7VNC3ZD9YD8). - [Deep Learning by Google](https://www.udacity.com/course/deep-learning--ud730) - Good intermediate to advanced-level course covering high-level deep learning concepts, I found it helps to get creative once the basics are acquired. - [Machine Learning
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Guillaume Chevalier · Canada
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Yog Mehta
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9e54c2d66e60548c, topic:awesome, topic:awesome-list, readme:course
matched fp:9e54c2d66e60548c, topic:deep-learning, topic:tensorflow