Deep Learning

Deep Learning (by Aaron Courville, Ian Goodfellow, and Yoshua Bengio) Read cover to cover, then applied these techniques to Crypto Markets (turns out, crypto markets are pretty inneficient)

1. The Goodfellow book is the obvious one. Another option is 'Machine Learning: A probabilistic perspective' by Kevin Murphy, which does have exercises but has less material on CNNs IIRC and is slightly older. Also Bishop 'Pattern Recognition and Machine Learning' is a classic reference but even older. 2. The Pedro Domingos book the Master Algorithm tries to address this itch / may be less philosophical that you want. It is >okay< but I don't think people love it.

I wrote a piece on that in: https://github.com/stared/thinking-in-tensors-writing-in-pyt... (https://github.com/stared/thinking-in-tensors-writing-in-pytorch#why-not-something-else) > If I wanted to learn some particular framework, I would just look up the documentation for that framework. Well, if you don't know deep learning, it is not how it works (unless it is a poor book, which only provides an introduction to some API). Still, I recommend "Deep Learning in Python" by Francois Chollet as it provides a good overview of practical deep learning. For practical applications, a book WILL use one framework or another or will be useless. If you understand overfitting, L2 or batch processing in Keras, you will be able to use in any other framework (after looking up its API). When it comes to the mathematical background, Deep Learning Book by Ian Goodfellow et al. is a great starting point, giving a lot of overview. Though, it requires a lot of interest in maths. Convolutional networks start well after page 300.…

I would read the Goodfellow book (though it is exactly as you describe). Beyond this, I would follow courses online depending on your interest, e.g.: - Stanford's CS231n (http://cs231n.stanford.edu) for Computer Vision - Stanford's CS224n (http://web.stanford.edu/class/cs224n/) for NLP They both have pretty solid exercises, which includes work like implementing back-propagation from first principles.

This for the background: https://mml-book.github.io/ This for actual methods: http://www.deeplearningbook.org/ This is also useful, but harder to read than the previous ones: https://web.stanford.edu/~hastie/Papers/ESLII.pdf

Seems like 'Deep Learning' [0] might suit for your first type of book. For the second type of book, may I suggest 'The Book of Why' by Judea Pearl. It isn't focused specifically on deep learning, but it is focused philosophical and cross disciplinary applications of statistical techniques. disclaimer: I haven't really dug in to deep learning, so I'll wager there may be great resources I'm completely unaware of. [0] 'Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press, https://www.deeplearningbook.org/

It is a little old given how quickly the field moves, but 'Information Theory, Inference and Learning Algorithms' has a chapter on neural networks. It is an outstanding book: a labour of love from a very smart person. The exercises are varied, explanations are great, there's a sprinkling of humour, and connections drawn between multiple fields of study. Moreover, it is freely available from the author's website: http://www.inference.org.uk/itprnn/book.html Have you considered giving Goodfellow another shot, but trying to re-derive the results therein as a form of exercise? I think that would likely be one of the faster methods to bring yourself reasonably up to date with the field.

I have both, and I agree these are great. I really appreciate the style of the Goodfellow book, it's very approachable.

AI: A Modern Approach (Russell, Norvig) and Deep Learning (Goodfellow, Bengio) have been mentioned already. I'd also recommend: Godel, Escher Bach: an Eternal Golden Braid by Douglas Hofstadter. Might not be exactly what you're looking for (it's all over the place, touching music theory, math, art, philosophy...), but it's fun and enjoyable to read. Also very dense.

You cannot really go practical in AI without academic rigor. You can do recipes of what's already been done using a TensorFlow book, but that's how far one can go. If one is serious in getting in AI today, a great way to do is read the following books, in order: 1. AI: A Modern Approach by Stuart Russell and Peter Norvig. 2. Deep Learning by Ian Goodfellow and Yoshua Bengio. It is amazing how approachable both books are for beginners, but you will be diving a lot into academic stuff as you go along.

Does ai count as CS? If so, I'd like to nominate the Deep Learning Book

Last year I gave myself a Zuckerberg-style goal to read a book every two weeks. It started well, but some months were harder than others (usually when the book wasn't very good, so I would switch to podcasts for a few weeks, before getting back on track). Here's an unordered list of the best: [BUSINESS / SELF-IMPROVEMENT] - "Work Rules!" - about Google's culture and values - "Search Inside Yourself" - about mindfulness, meditation, and the impact on your life. - "What got you here won't get you there" - Liked a lot more than I expected. - "Re-read Innovator's Dilemma" - a great classic; never gets old. - "How will you measure your life" - from the same author. Also excellent. - "The Best Service is No Service" - good overview on why Amazon's customer service is so good - "Crucial Conversations" - half is common sense; half is good tips to practice everyday. [PROGRAMMING] - "Fluent Python" - was afraid it'd be too basic, but not really. Enjoyed it a lot. - "Think Complexity" - about algorithm complexity, data structures, etc. Great read. - "Python Data Science Handbook" - meh. - "Fundamentals of Deep Learning" - good intro. Helped me solidify some concepts from Andrew Ng's ML course…