How would you compare this book to the first part of "Deep Learning" book (by Ian Goodfellow, Yoshua Bengio, and Aaron Courville)? https://www.amazon.com/gp/product/0262035618

The list does not describe why they are the best books, except for a very short blurb. We read the Deep Learning book by Goodfellow, Bengio, and Courville in our reading group when it came out. Even though it contains useful information, it is written in a very haphazard fashion. It is also very unclear what its target audience is. Some sections start as a foundational description, to suddenly change into something that is only for readers with a strong maths background. No one in the reading group was enthusiastic about the book and most actively recommend against it (some called it 'the deep learning book for people who already know deep learning').

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.

Would you enjoy something that gives a broad overview? Norvig's AI book https://www.amazon.com/Artificial-Intelligence-Modern-Approa... should give you a very broad perspective of the entire field. There will be many course websites with lecture material and lectures to go along with it that you may find useful.

The book website http://aima.cs.berkeley.edu/ has lots of resources.

But it sounds like you are specifically interested in deep learning. A Google researcher wrote a book on deep learning in Python aimed at a general audience - 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.)

In machine learning, hands down these are some of the best related textbooks:

- [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](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%...)

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

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

- [2] [site](https://www.deeplearningbook.org/)

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

- [3] [pdf](http://incompleteideas.net/book/bookdraft2017nov5.pdf)

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

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

I made the same transition earlier in my career. One book on deep learning that meets your requirements is [0]. It’s readable, covers a broad set of modern topics, and has pragmatic tips for real use cases.

For general machine learning, there are many, many books. A good intro is [1] and a more comprehensive, reference sort of book is [2]. Frankly, by this point, even reading the documentation and user guide of scikit-learn has a fairly good mathematical presentation of many algorithms. Another good reference book is [3].

Finally, I would also recommend supplementing some of that stuff with Bayesian analysis, which can address many of the same problems, or be intermixed with machine learning algorithms, but which is important for a lot of other reasons too (MCMC sampling, hierarchical regression, small data problems). For that I would recommend [4] and [5].

Stay away from bootcamps or books or lectures that seem overly branded with “data science.” This usually means more focus on data pipeline tooling, data cleaning, shallow details about a specific software package, and side tasks like wrapping something in a webservice.

That stuff is extremely easy to learn on the job and usually needs to be tailored differently for every different project or employer, so it’s a relative waste of time unless it is the only way you can get a job.

[0]: < https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma... >

[1]: < https://www.amazon.com/Pattern-Classification-Pt-1-Richard-D... >

[2]: < https://www.amazon.com/Pattern-Recognition-Learning-Informat... >

[3]: < http://www.web.stanford.edu/~hastie/ElemStatLearn/ >

[4]: < http://www.stat.columbia.edu/~gelman/book/ >

[5]: < http://www.stat.columbia.edu/~gelman/arm/ >

Our vision is to be the tool that starts with great settings for beginners but lets you graduate into the internals as you become more expert - at the lowest level you can interactively create computation graphs and see their results as you change settings, sort of like eager mode for ml frameworks on steroids (or other visual computation graph programs that designers use like Origami/Quartz Composer).

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 (http://cs231n.stanford.edu/) and the Goodfellow/Bengio/Courville Deep Learning book (https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...)

Here’s the book that’s mentioned:

http://www.deeplearningbook.org/

Seems to have good reviews on Amazon:

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

Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow, Yoshua Bengio, Aaron Courville

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...

I'll give you a couple. Note that some of these are rehashes of my earlier comments.

# 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.youtube.com/watch?v=4moyKUHApq4&t=39m30s

Another point of his is that the explosion of written code we are seeing isn't sustainable and that so much of this code is algorithms or data structures with overlapping functionalities. As the codebases grow, and these functionalities diverge even further, pulling the reigns in on the chaos becomes gradually impossible.

Bjarne Stroustrup (aka the C++ OG) gave this book five stars on Amazon (in what is his one and only Amazon product review lol).

This style might become dominant because it's only really possible in modern successors of C++ such as Swift or Rust, not so much in C++ itself.

https://smile.amazon.com/review/R1MG7U1LR7FK6/

# Grammar of graphics

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/gp/product/0262660717/ref=as_li_ss_tl...

Not done with the book but despite it's age, hands down best intro to category theory if you care about it only for CS purposes as it tries to show how to apply the concepts. Very concise (~70 pages).

# Markov Logic: An Interface Layer for Artificial Intelligence

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/gp/product/0262018020/ref=as_li_ss_tl...

Exhaustive overview of the entire field of machine learning. It's engaging and full of graphics.

# Deep Learning

https://www.amazon.com/gp/product/0262035618/ref=as_li_ss_tl...

http://www.deeplearningbook.org/

You probably have heard about this whole "deep learning" meme. This book is a pretty self-contained intro into the state of the art of deep learning.

# Designing for Scalability with Erlang/OTP: Implement Robust, Fault-Tolerant Systems

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/gp/product/1107150302/ref=as_li_ss_tl...

Not much to say, probably THE book on programming language theory.

# A First Course in Network Theory

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.