vkramdev.github.io

List of Open Source Resources for ML/DL

Listing of repository for the resources i find interesting and useful in the path to learn and implement Machine Learning. This is in process.

I plan to update this on weekends.

Resources:

Others:

Imp: John Krohn: https://www.jonkrohn.com/posts/2020/4/14/18-hours-of-brand-new-video-tutorials-introducing-all-of-deep-learning

1 : https://jeremykun.com/

2. Fall 2015 STAT 946: Deep Learning: https://www.youtube.com/playlist?list=PLehuLRPyt1Hyi78UOkMPWCGRxGcA9NVOE

3. 11-785 Introduction to Deep Learning | Carnegie Mellon University | Spring 2020 :https://www.youtube.com/playlist?list=PLp-0K3kfddPzCnS4CqKphh-zT3aDwybDe

4. Machine Learning Tokyo: https://www.youtube.com/MLTOKYO github: https://github.com/Machine-Learning-Tokyo/CNN-Architectures/tree/master/Implementations https://github.com/Machine-Learning-Tokyo/AI_Curriculum website: https://machinelearningtokyo.com/

5. https://madewithml.com/

6. Interactive AI journal: https://distill.pub/

7. @twitter: https://twitter.com/omarsar0/status/1214547402838986754 Chip Huyen: https://twitter.com/chipro/status/1157772112876060672 https://twitter.com/sannykimchi/status/1138103256792494085 Rachel : https://twitter.com/math_rachel/status/1125545968730918912 Jalammar :https://jalammar.github.io/ Sebastian Ruder : https://ruder.io/ Andrej Karpathy : https://karpathy.github.io/ Resource Meta List:

   https://sgfin.github.io/learning-resources/

8. https://web.stanford.edu/~jurafsky/slp3/

9. https://www.h2o.ai/wp-content/uploads/2019/08/An-Introduction-to-Machine-Learning-Interpretability-Second-Edition.pdf

10. http://kagglesolutions.com/

11. https://www.youtube.com/watch?v=7wy0jqJU8ts&feature=youtu.be

12. https://cbmm.mit.edu/video/getting-started-tensorflow-20-tutorial

13. https://pjreddie.com/courses/computer-vision/

14. https://inst.eecs.berkeley.edu/~cs188/fa18/

15. https://sites.google.com/view/berkeley-cs294-158-sp20/home

16. Blogs:

https://medium.com/mlreview/machine-learning-on-graphs-neurips-2019-875eecd41069

Python:

1.David Beazley(@dabeaz) Practical Python Programming: https://dabeaz-course.github.io/practical-python/

2. Python Data Science Handbook(Jake Vanderplas): https://jakevdp.github.io/PythonDataScienceHandbook/

3. https://code-love.com/2019/06/03/49-essential-resources-to-learn-python/

ML models to productions:

1. https://course.fullstackdeeplearning.com/

Mathematics and Stats for ML:

1. Paul G ALLEN 2019 Mathematics of Machine Learning Summer School: https://www.youtube.com/playlist?list=PLTPQEx-31JXhguCush5J7OGnEORofoCW9

Machine Learning Tutorials and courses:

1. Stanford CS229: Machine Learning :https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU

2.Caltech CMS 165 Lectures: Foundations of Machine Learning: https://www.youtube.com/playlist?list=PLVNifWxslHCA5GUh0o92neMiWiQiGVFqp Course website: http://tensorlab.cms.caltech.edu/users/anima/cms165-2019.html

3. Andreas Muller Applied ML: https://www.youtube.com/playlist?list=PL_pVmAaAnxIQGzQS2oI3OWEPT-dpmwTfA

Deep Learning Tutorials and courses:

1. Deep Learning Book Companion Videos : https://www.youtube.com/playlist?list=PLsXu9MHQGs8df5A4PzQGw-kfviylC-R9b Book : https://www.deeplearningbook.org/

2. Frontiers of Deep Learning: https://www.youtube.com/playlist?list=PLgKuh-lKre11ekU7g-Z_qsvjDD8cT-hi9

3. Workshop on Theory of Deep Learning(IAA): https://www.youtube.com/playlist?list=PLdDZb3TwJPZ5dqqg_S-rgJqSFeH4DQqFQ

4. Deep Learning (with PyTorch) NYU Yann LeCunn&Alfredo Canziani: https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq https://atcold.github.io/pytorch-Deep-Learning/ Lecture: https://youtu.be/--NZb480zlg Practicum: https://youtu.be/eEzCZnOFU1w Transcript: http://bit.ly/pDL-en-05

5. Geoffret Hinton's UoT Neural Network For ML: https://www.youtube.com/playlist?list=PLLssT5z_DsK_gyrQ_biidwvPYCRNGI3iv

6. François Fleuret(@francoisfleuret): https://fleuret.org/ee559-2018/dlc/

7. Deep Unsupervised Learning -- Berkeley Spring 2020: https://www.youtube.com/playlist?list=PLwRJQ4m4UJjPiJP3691u-qWwPGVKzSlNP

8. FastAi: https://www.fast.ai/

9. Dive into Deep Learning(Book ): https://d2l.ai/

10. http://introtodeeplearning.com/

NLP:

1 https://www.victorzhong.com/blog/getting-started-in-NLP-ML-research.html

2. CMU LTI Low Resource NLP Bootcamp 2020: https://github.com/neubig/lowresource-nlp-bootcamp-2020

3. Mihail Eric: https://www.mihaileric.com/posts/complete-artificial-intelligence-undergraduate-course-plan/

4.Stanford NLU: https://online.stanford.edu/courses/xcs224u-natural-language-understanding

NLP Blogs: 5.1 https://nlpoverview.com/ 5.2 https://research.utwente.nl/en/publications/text-as-social-and-cultural-data-a-computational-perspective-on-v

ML/DL in Production:

MadeWithML : https://github.com/madewithml/applied-ml-in-production

Free eBooks for ML/DL:

1. https://pytorch.org/deep-learning-with-pytorch : pytorch is giving free ebook.Fill in the form and receive a free copy.

ML/DL papers:

Spark:

Spark+AI 2020:https://databricks.com/sparkaisummit/north-america-2020/agenda