# Deep Learning (Adaptive Computation and Machine Learning series)

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The highest-rated Amazon reviews seem to have come to the same conclusion: https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...

Put differently, a list such as the linked one may attract a lot of visitors. But without critical, in-depth reviews it is not very useful and might set potential learners on the wrong path.

[1] https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...

[2] https://www.barnesandnoble.com/w/deep-learning-ian-goodfello...

[3] https://www.deeplearningbook.org/

The book website https://www.amazon.com/Deep-Learning-Python-Francois-Chollet... - which might be more directly relevant to your interests.

There's also what I guess you would call "the deep learning book". https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...

(People have different preferences for how they like to learn and as you can see I like learning from books.)

(I apologize if you already knew about these things.)

The lobes in the UI are all essentially functions that you double click into to see the graph they use, all the way down to the theory/math.

If you want more comprehensive ways to learn the theory, I highly recommend Stanford's 231n course (https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...)

- [0] Pattern Recognition and Machine Learning (Information Science and Statistics)

and also:

- [1] The Elements of Statistical Learning

- [2] Reinforcement Learning: An Introduction by Barto and Sutton

- [3] The Deep Learning by Aaron Courville, Ian Goodfellow, and Yoshua Bengio

- [4] Neural Network Methods for Natural Language Processing (Synthesis Lectures on Human Language Technologies) by Yoav Goldberg

Then some math tid-bits:

[5] Introduction to Linear Algebra by Strang

----------- links:

- [0] [PDF](https://www.amazon.com/Pattern-Recognition-Learning-Informat...)

- [2] [amz](https://www.amazon.com/Reinforcement-Learning-Introduction-A...)

- [2] [site](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...)

- [3] [pdf](https://www.amazon.com/Language-Processing-Synthesis-Lecture...)

- [5] [amz](https://www.amazon.com/Introduction-Linear-Algebra-Gilbert-S...)

https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...

# Elements of Programming

https://www.amazon.com/Elements-Programming-Alexander-Stepan...

This book proposes how to write C++-ish code in a mathematical way that makes all your code terse. In this talk, Sean Parent, at that time working on Adobe Photoshop, estimated that the PS codebase could be reduced from 3,000,000 LOC to 30,000 LOC (=100x!!) if they followed ideas from the book https://www.amazon.com/Grammar-Graphics-Statistics-Computing...

This book changed my perception of creativity, aesthetics and mathematics and their relationships. Fundamentally, the book provides all the diverse tools to give you confidence that your graphics are mathematically sound and visually pleasing. After reading this, Tufte just doesn't cut it anymore. It's such a weird book because it talks about topics as disparate Bayesian rule, OOP, color theory, SQL, chaotic models of time (lolwut), style-sheet language design and a bjillion other topics but always somehow all of these are very relevant. It's like if Bret Victor was a book, a tour de force of polymathical insanity.

The book is in full color and it has some of the nicest looking and most instructive graphics I've ever seen even for things that I understand, such as Central Limit Theorem. It makes sense the the best graphics would be in the book written by the guy who wrote a book on how to do visualizations mathematically. The book is also interesting if you are doing any sort of UI interfaces, because UI interfaces are definitely just a subset of graphical visualizations.

# Scala for Machine Learning

https://www.amazon.com/Scala-Machine-Learning-Patrick-Nicola...

This book almost never gets mentioned but it's a superb intro to machine learning if you dig types, scalable back-ends or JVM.

It’s the only ML book that I’ve seen that contains the word monad so if you sometimes get a hankering for some monading (esp. in the context of ML pipelines), look no further.

Discusses setup of actual large scale ML pipelines using modern concurrency primitives such as actors using the Akka framework.

# Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...

Not released yet but I've been reading the drafts and it's a nice intro to machine learning using modern ML frameworks, TensorFlow and Scikit-Learn.

# Basic Category Theory for Computer Scientists

https://www.amazon.com/Markov-Logic-Interface-Artificial-Int...

Have you ever wondered what's the relationship between machine learning and logic? If so look no further.

# Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

https://www.amazon.com/Designing-Scalability-Erlang-OTP-Faul...

Even though this is an Erlang book (I don't really know Erlang), 1/3 of the book is devoted to designing scalable and robust distributed systems in a general setting which I found the book worth it on it's own.

# Practical Foundations for Programming Languages

https://www.amazon.com/First-Course-Network-Theory/dp/019872...

Up until recently I didn't know the difference between graphs and networks. But look at me now, I still don't but at least I have a book on it.

Came out in November 2016. Split in 3 parts:

Part I: Applied Math and Machine Learning Basics (Linear Algebra, Probability and Information Theory, Numerical computation)

Part II: Deep Networks: Modern Practices (Deep Feedforward Networks, Regularization, CNNs, RNNs, Practical Methodology & Applications)

Part III: Deep Learning Research (Linear Factor Models, Autoencoders, Representation Learning, Structured Probabilistic Models, Monte Carlo Methods, Inference, Partition Function, Deep Generative Models)

https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...