The first principle is that you must not fool yourself — and you are the easiest person to fool.

The obvious question expected to get answered when you visit any About page is “Who Are You?”

But that’s not what this page is about.

What happens when you ask someone, “What is the best way to learn deep learning?” You will most likely be bombared with tons of courses online. What the question meant was, “Where to start?”

IMO the best way to learn data science is not through earning a degree(nothing wrong with that). I mean weigh all the pros you can get through MOOCs, that is to learn from so many different courses from so many awesome professors around the world not bound to specific university. It’s like we can go to MIT for one course, Stanford or Harvard for another, CMU or Cornell or land in USF next day for another. I mean how cool is that! And the best thing is all that is free of cost, no traveling, no accommodation, now what can you ask more for?(Ask Geoffrey Hinton to teach every course covering every topic 😛)

Okay enough talk, I tried to declutter various sources and arrange it in such a way that anyone can start learning about DL if put in enough efforts and complete every course + assignments religiously. But before that, remember what Richard Feynman said about knowing the name of something and understanding it.

See that bird? It’s a brown-throated thrush, but in Germany it’s called a halzenfugel, and in Chinese they call it a chung ling and even if you know all those names for it, you still know nothing about the bird. You only know something about people; what they call the bird. Now that thrush sings, and teaches its young to fly, and flies so many miles away during the summer across the country, and nobody knows how it finds its way.

Knowing the name of something doesn’t mean you understand it. The first and foremost is to understand the difference between DL, ML and AI.

Here I present to you the list of courses that I would recommend with increasing level of difficulty. I will keep updating the table with latest courses as they come.

Casual Hardcore Insane
Coursera Machine Learning or CS229 notes Stanford CS231n: Convolutional Neural Networks for Visual Recognition, Winter 2016 and Spring 2017 UCL Advanced Deep Learning & Reinforcement Learning
fast.ai Introduction to Machine Learning for Coders! Stanford CS224N: NLP with Deep Learning, Winter 2019 Cornell CS4780: Machine Learning for Intelligent Systems, Spring 2017
Deeplearning.ai Specialization Stanford CS224U: Natural Language Understanding, Spring 2019 University of Waterloo CS885: Reinforcement Learning, Spring 2018
TensorFlow Specialization by deeplearning.ai UCL Reinforcement Learning, DeepMind x UCL: Deep Learning Lecturse University of California, Berkeley CS294-158: Deep Unsupervised Learning, Spring 2019
Introduction to Deep Learning with PyTorch Stanford CS234: Reinforcement Learning, Winter 2019 CMU Neural Nets for NLP 2019
Stanford CS230: Deep Learning, Autumn 2018 Applied Machine Learning 2020 Alberta Machine Intelligence Institute Reinforcement Learning Specialization
2019 fast.ai Practical Deep Learning for Coders v3 part 1 and part 2 2019 fast.ai course: A Code-First Introduction to Natural Language Processing Stanford CS330: Deep Multi-Task and Meta Learning
AI Education Resources by Google The Ancient Secrets of Computer Vision  

Do you want to learn DL all day? Here are some of my favourite “Unwinding Tracks” which you can switch to avoid information overload 🤯.

Learning How to Learn - Barbara Oakley and Richard Hamming: Learning to Learn Justice with Michael Sandel A Brief History of Humankind - Yuval Noah Harari
The Ethics and Governance of Artificial Intelligence Calling Bullshit in the Age of Big Data Quantum computing for the determined qcvc part1, part2 and part3
MIT 6.868J: The Society of Mind, Fall 2011 MIT 6.034: Artificial Intelligence, Fall 2010 MIT 18.065: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
MIT 9.11: The Human Brain Full Stack Deep Learning Bootcamp Project Tuva: Richard Feynman’s Messenger Lecture Series
Principal wise-ass Paul Graham Essays The Pmarca Blog
Human Behavioral Biology Every video by 3Blue1Brown, ben eater Khan Academy: Linear Algebra and Multivariable Calculus

And what about any recommendation for books📚 ?

Deep Learning Book Neural Networks and Deep Learning Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
Deep Learning with Python (Keras) Reinforcement Learning: An Introduction (2nd Edition) Artificial Intelligence: A Modern Approach
Deep Learning for Computer Vision with Python Machine Learning Yearning Speech and Language Processing
Pattern Recognition and Machine Learning Grokking Deep Learning Python Machine Learning Sebastian Raschka
Dive into Deep Learning Draft: Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD Deep Learninh with PyTorch