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rouse on Feb 4, 2018 | hide | past | web | favorite



Or... Just do the fast.ai course and then find a project that interests you and learn, on your own (with the many resources that are available across the internet), what you need to solve your problem.

I see that you have a capstone entry at the end of your list, but learning with no immediate goal or project to apply it to seems like the quickest way to burn out on what can be a challenging topic for most.

For more on this listen to (https://youtu.be/IPBSB1HLNLo?t=31m2s)


Good post. I want to add several great resources.

- Grokking Deep Learning

https://www.manning.com/books/grokking-deep-learning

This is a fantastic book that assumes no prerequisites other than knowing python, and takes you through the fundamentals of DL. It has very intuitive and easy to follow explanations, and doesn't use any libraries other than NumPy, so you're building the whole thing yourself, from scratch.

- Deep Learning With Python

https://machinelearningmastery.com/deep-learning-with-python...

This is kind of the opposite of the previous one, it doesn't go into math and theory, instead it guides you through building several practical projects with a very simple to use DL library(keras). It's a great way to gain practical experience in addition to theory from the previous book. Also has no prerequisites other than python, and makes it very easy to get started.

- 3blue1brown videos on neural networks:

https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_6700...

Extremely brilliant high-level concise overview of how ANNs work. I highly recommend you get started here. You should also check out his videos on calulus and linear algebra, they're fantastic way to learn the math you need.

- Khan Academy videos - one of the easiest ways to learn the math prerequisites.

Calculus:

https://www.khanacademy.org/math/calculus-home

Linear Algebra:

https://www.khanacademy.org/math/linear-algebra

Probability and Statistics:

https://www.khanacademy.org/math/statistics-probability

- Hands-On Machine Learning with Scikit-Learn and TensorFlow

http://shop.oreilly.com/product/0636920052289.do

I haven't read this one yet, but it looks very promising, and a lot of people seem to find it very useful.

- Andrew Ng's Coursera course

https://www.coursera.org/learn/machine-learning

Everyone knows about this one, I just think every article on AI resources should mention it, one of the most popular ways to get started with ML.

- New MIT courses on Self-Driving cars and AGI

https://selfdrivingcars.mit.edu

https://agi.mit.edu

- The Master Algorithm

https://www.audible.com/pd/Science-Technology/The-Master-Alg...

Excellent high-level overview of ML field and algorithms.

====

Other great stuff:

- Artificial Intelligence: A Modern Approach

http://aima.cs.berkeley.edu/

The leading textbook in Artificial Intelligence. It's not the fastest way to get started, but it's considered one of the best AI textbooks ever written.

- Stanford AI course (CS 188)

https://www.youtube.com/playlist?list=PLIeooNSdhQE5kRrB71yu5...

Brilliant course based on AIMA. Not DL, but solid fundamentals of AI and ML.

- Couple of great playlists on DL, just to complete the collection:

Machine Learning with Python

https://www.youtube.com/playlist?list=PLQVvvaa0QuDfKTOs3Keq_...

Neural Networks Demystified

https://www.youtube.com/watch?v=bxe2T-V8XRs&index=1&list=PLi...


Really good article for someone who is interested in starting their deep learning career.




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