Torch Learn is my ongoing project that documents all the resources I have found useful, all the small projects I have built, and all the white papers I have found interesting. This small project is meant for sharing with the rest of the academia and all beginners to Statistics & Machine Learning.
Feel free to fork and make your own. I only ask you give credit to where it is due.
Happy learning \:-)
Disclaimer: I do not own any of the resources listed below. Please use them responsibly and adhere to respective ©Copyright and related laws. I am not responsible for any plagiarism or legal implication that rises as a result of this GitHub repository.
- Denotes in Progress
- Denotes Completed
Awesome Reinforcement Learning
Many thanks to Siraj Raval, how practically inspired me to create this list to track my learning.
Siraj: Learn Deep Learning in 6 Weeks
Siraj: Learning CS in 5 Months
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I have been a TA for this course, and I can attest to its effectiveness in teaching basic CS concepts in python. Begin with this if you have absolutely no programming experience.
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15-112 has a notorious/loved fame at CMU because of its rigorous content and great professors. Will be very challenging but will prepare you well.
- 3Blue1Brown: Linear Algebra
- Good for intuition training, but you will need more practice afterwards.
- Any same person would know not to watch all 144 videos. Use it for hard concepts and practices.
Coursera: ML, by Stanford, Andrew Ng
- Pytorch Official
- The folks at PyTorch have wonderful tutorials. Check them out.
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DeepLizard: Neural Network Programming - Deep Learning with PyTorch
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A surprisingly super clear playlist for PyTorch. Yuo folks at DeepLizard deserve more subs.
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The famous fast.ai has hours of worthwhile tutorials. Check them out.
edx: Deep Learning with Python and PyTorch
CS231n: Convolutional Neural Networks for Visual Recognition
CS224: Natural Language Processing with Deep Learning
edx: Applied Deep Learning Capstone Project, by Microsoft
Udacity: Reinforcement Learning, by Georgia Tech
- This course is in Java.
Before you jump in, please remember to follow the leaders in ML on Facebook, Twitter, LinkedIn, and other social media. Remember, if you really love this field, you need to make it part of your life-long learning journey.
- Duh.
- Probably simplest and most beginner-friendly.
Personally, I follow renowned ML researchers, labs, and institutes on FaceBook and Twitter. These include but not limited to:
ML Researchers & CS Professors
- Andrew Ng
- Ian Goodfellow
- Yan LeCun
- Yoshua Bengio
- Jeoffery Hinton
- Hardmaru (Google Tokyo)
Institutes & Industry Labs
- MIT CS
- SCSatCMU
- MLatCMU
- Google Brains
- Facebook AI Research
Disclaimer: I particularly do not own any of the resources above. I try my best to give you the original link, but sometimes I do lose or forget them for ages. In that case, I may have it downloaded already and available on this repository. I am not responsible for any use of the below resources. I am happy to remove such resources upon contact in case of ©copyright regulations.
Applying to Ph.D. Programs in Computer Science
A Few Useful Things to Know about Machine Learning
The Discipline of Machine Learning
Mathematics for Machine Learning
Computer Vision: Algorithms and Applications (Richard Szeliski)