Comprehensive Deep learning education from MIT by Lex Fridman. New lectures started from January of this year. Videos and Slides are available.

Github repository of lecture series:

https://github.com/lexfridman/mit-deep-learning?fbclid=IwAR0jLFfsHt4k_7Y3QfT-leXoIhCoDlg3bzjXyBxDX01hJcJkDtKS340OaQU

The world’s easiest introduction to Machine Learning by Adam Geitgey. Covered every applications of Machine Learning and translated version also avialable in several languages. Enjoy and boost your knowledge

Python 3 Object Oriented Programming series by Leaonardo Giordani. Important implementation concepts and boost your programming skills by practising

What goes on in the mind of BERT? Using an interactive visualization tool, we uncovered some surprisingly intuitive patterns.

Article is here: https://towardsdatascience.com/deconstructing-bert-distilling-6-patterns-from-100-million-parameters-b49113672f77?fbclid=IwAR35FclDpV-PqlWyfPKFqbXuy8YEpdNtuXGTKTl5bBnNKymeM0Ak9p4I-pk

Google Colab code is here: https://colab.research.google.com/drive/1vlOJ1lhdujVjfH857hvYKIdKPTD9Kid8?fbclid=IwAR2bQZMqXXXCYE3z9FCdRyKwfMzs4XVsF70fGkwMMFsIl0ClGGecN_BJ-gs

This picture is made of 195 billion pixels and all of the pixels are incredible and clear detail. Typical phone camera snaps 12-megapixel photos(12 million pixel). Just another advance of Computer Science. It also opens new challenges and research areas in the fields of #AI, #ML, #DL, #CV.
Thanks for Seeker and BigPixel Studios for creat video

This week in Machine Learning and AI. Shows, Series, Ebooks, Meetup, Community Programs... Extremely organized and detail-oriented. Keep up trends of #ML, #AI

Must-read tutorial to learn sequence modeling from 'Analytics Vidhya' team and detailed explanation by real examples

Ultimate learning path of Deep Learning for 2019 in infographics. Image credited from Analytics Vidhya

Prediction based algorithms in infographics. Type, name, description, advantages and disadvantages

This is a super cool resource: Papers With Code now includes 950+ ML tasks, 500+ evaluation tables (including SOTA results) and 8500+ papers with code. Probably the largest collection of NLP tasks I've seen including 140+ tasks and 100 datasets.

Impractical PythonProjects by Lee Vaughan 2018

Github repository:
https://github.com/rlvaugh/Impractical_Python_Projects