Found 5 comments on HN
rayalez · 2018-02-04 · Original thread
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...

jacek · 2018-01-13 · Original thread
Not a video course, but an excellent book: "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Aurélien Géron. Provides a gentle and high quality introduction to Machine Learning with practice from the very beginning. I wish I had this book when I was starting. It explains everything from data engineering, through how algorithms work, to practical applications. Everything in Python 3, covering pandas, scikit-learn, tensorflow. It is absolutely wonderful!

[1] http://shop.oreilly.com/product/0636920052289.do

jacek · 2017-12-26 · Original thread
If someone wants a gentle and high quality introduction to Machine Learning, I can't recommend " Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Aurélien Géron [1] enough. I wish I had this book when I was starting. It explains everything from data engineering, through how algorithms work, to practical applications. Everything in Python 3, covering pandas, scikit-learn, tensorflow. It is absolutely wonderful!

[1] http://shop.oreilly.com/product/0636920052289.do

AlexCoventry · 2017-12-06 · Original thread
Try Hands-On Machine Learning with Scikit-Learn and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems for the fundamentals.

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

For reinforcement learning, I hear Barto and Sutton is very readable, but I haven't read it myself. You can just pick the concepts up by reading papers. The introduction in the Deep Q-Learning paper is not great, but it's how I first learned the concept.

http://ufal.mff.cuni.cz/~straka/courses/npfl114/2016/sutton-... https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf

atsheehan · 2016-11-11 · Original thread
If anyone is interested in learning more about scikit-learn, I'd recommend "Hands-On Machine Learning with Scikit-Learn and Tensorflow" from O'Reilly:

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

When I first started using scikit-learn, I was overwhelmed with the number of classes and options available. I just chose some basic classifiers I was familiar with and stuck with most of the default settings. The book explains many of the other models and when they would be useful, but also spends a lot of time exploring the datasets (using pandas), preprocessing data and building data pipelines, finding the best hyperparameters, best ways to evaluate a models performance, etc. The library feels less like a big bag of algorithms now and more like a cohesive data pipeline.

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