I loved Deep Learning with Python by François Chollet -- the creator of Keras

https://www.manning.com/books/deep-learning-with-python

An excellent, coherent overview with working code to train a variety of neural networks.

Consider the free PDF of Chollet's "Deep Learning with Python" and/or www.fast.ai

Deep Learning with Python by Francois Chollet is very accessible as an introductory text. It uses plain language, avoids heavy math, and provides hands-on experience for the reader.

In general, I have found Manning to be one of the best technical publishers in terms of quality of content and updates.

I cannot recommend Deep Learning with Python [0] enough. It is written by the author of the Keras library.

[0]: https://www.manning.com/books/deep-learning-with-python

I found the Manning book 'Deep Learning with Python' by François Chollet (creator of Keras) a fantastically digestible and practical intro.

My favourite part of this is that Francois Chollet (the author of Keras) specifically warns against this exact use of LSTMs in his book (Deep Learning with Python)

While there are some good picks (especially "An Introduction to Statistical Learning with Applications in R" and "Deep Learning with Python" by Chollet, the Keras author), I am surprised it is missing "Information Theory, Inference, and Learning Algorithms" by David MacKay (http://www.inference.org.uk/itila/book.html).

The "Bayesian Inference and Machine Learning" track gives a nice foundation to anything with "log loss". After that even k-means won't be an ad-hoc algorithm.

> What should I choose, pytorch or keras?

I know this is off-topic, but you can't go wrong with Keras. "Deep Learning with Python" by Francois Chollet (the creator of Keras) is a Kernighan-level book. A book like this comes around once a decade (or two). If you decide to go for it though, make sure you don't follow its instructions about the amazon cloud deep learning VM's; you'll end up paying $10 per day, even if you stop the VM.

Recently I have bought the book "Deep Learning with Python"[1], and I can't recommend it enough. Very gentle introduction into deep learning, through creating several practical projects. If you know the basics of ML - you should get it, it's amazing.

To learn the basics of ML, you can check out the awesome tuts+ course[2].

Also I wrote an article [3] with the collection of the best free resources, I think you'll find it useful.

[1] https://machinelearningmastery.com/deep-learning-with-python...

[2] http://code.tutsplus.com/courses/machine-learning-distilled

[3] https://medium.com/@rayalez/list-of-the-best-resources-to-le...

I think that this is a horribly impractical advice, and I keep seeing it everywhere.

With modern tools and frameworks you can start learning and applying what you know on practice almost immediately.

Check out Keras and the book "Deep Learning with Python"[1]. They have enabled me to train my first ANN in 2 days, and get to the point of building a MNIST recognizer in a month(and I was reading it pretty slowly).

Sure, if you're coding it from scratch and must understand every signle detail, you do need like 10 years and 3 PhD's. But that's not a wise way to learn.

I recommend to take the simplest tools, and apply them to practical projects immediately. That will give you the general overview of how things work, and then you will learn the details as needed.

[1] https://machinelearningmastery.com/deep-learning-with-python...

If you are just starting with ML a very good start IMO is "Deep learning with Python", by Chollet. The second edition is very recent.

It starts from the basics and takes you to some complex scenarios. It's focused primarily on Keras, which is a very to use library to start from.

The book covers a lot ML on images, so moving from there to generative art should be "easy" once you grasp the fundamentals.

I don't know however how feasible is to do ML on Macs, it might be a breeze or impossible, I genuinely have no idea :-)

Deep Learning with Python (Chollet) and Hands-On Machine Learning (Géron) may be a bit redundant currently. With 2.x versions of Tensorflow, Keras (which is what Deep Learning with Python covers) has been completely integrated into into the Tensorflow API (which is covered in Hands-On Machine Learning). Both books are good, but the newest edition of Hands-On Machine Learning is updated for Tensorflow 2.0, and so it is probably the more relevant of the two.

Things I started to learn:

- knitr, as I got sick of LaTeX (I always ended up fiddling with the figure placement forever until it was just right). I also moved all my R work into RStudio, which is a HUGE time saver and the Projects function has saved me a few times.

- snakemake, for reproducible bioinformatics workflows, integrates nicely with HPC systems, and is very beautiful (the few times I've used it)

- I went through the first fast.ai course, and used it with some success on kaggle, but I'm far from doing 'bleeding edge' stuff I think (applied is fine though! give me a nice dataset and I should be fine). I've also read the Kaggle book (Deep Learning with Python), which contains a whole bunch of these weird useful machine learning tips that no-one really can't explain mathematically

- I've also made an effort to switch my R code to immediately use as much tidyverse as possible, which isn't always OK when using bioconductor at the same time

That's why all the hype about DL is so irritating. Yes, it allowed us to make enormous progress in certain fields. No, it's nowhere near usable in others. So why pretend it is?

Just one example from the preface to Chollet's "Deep Learning with Python":

> If you’ve picked up this book, you’re probably aware of the extraordinary progress that deep learning has represented for the field of artificial intelligence in the recent past. In a mere five years, we’ve gone from near-unusable image recognition and speech transcription, to superhuman performance on these tasks.

Come on, speech to text is still far from usable unless in a very limited scenarios. Why pretend it's different?

Francois Cholet (author of Keras)'s Deep Learning with Python will be complete and fully published on the 20th of December: https://www.manning.com/books/deep-learning-with-python The chapters released so far are very good, the outlook chapters were extensively discussed on HN: https://blog.keras.io/the-limitations-of-deep-learning.html and https://blog.keras.io/the-future-of-deep-learning.html

Wickham's R for Data Science came out in December 2016, I'd like to pretend that counts as 2017: http://r4ds.had.co.nz/
It's a very complete introduction to the tidyverse which makes working in R much more pleasurable.

The second edition of Python for Data Science came out in October 2017 - that one focuses on Pandas, numpy and Jupyter notebooks, reasonably good introduction to those libraries.

The second edition of Sebastian Raschka's Python Machine Learning came out 2017 too - that one focuses more on scikit-learn and tensorflow, have only heard good things but haven't read much in it.

I have Deep Learning with Python (Chollet, first edition) and Hands-On Machine Learning (Géron, first edition). Both books are highly recommended.

Introduction to Statistical Learning is also available for free online:

http://faculty.marshall.usc.edu/gareth-james/ISL/

Although I only read a few chapters from that book, I really like it (but I would have preferred a python version of the book).

Personally, if you have to pick three books from the list, ypu can start with these three options.

I would highly recommend going through François chollet's Deep Learning with Python[1] book. The technical concepts are explained very well and since you have gone through the Modern AI book you won't have an issue understanding them. It's a very hands-on book and by the time you finish it you will be able to use Deep Neural Nets to solve many problems.

I would also recommend going through the scikit-learn documentation. Some of the tutorials/examples there are pretty good.

At the end of the day, it all comes down to your personal learning style. For me the thing that worked was to go through the above mentioned steps and then find a problem I was interested in and try to solve it using my newly found skills. That way you will discover new tools and methods.

Finally, the Deep Learning [2] book is also very good but I would not recommend it to a beginner. It's better to use it when you have a basic understanding of Machine Learning and you want to gain a deeper understanding of the concepts.

[1]:https://www.manning.com/books/deep-learning-with-python

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

More great Jupyter Notebooks in the AI field:

1) 16 notebooks from the book "Python Machine Learning" by Raschka & Mirjalili
https://github.com/rasbt/python-machine-learning-book-2nd-ed...

2) Linear Regression, Logistic Regression, Random Forests, and k-Means Clustering notebooks by Nitin Borwankar
https://github.com/nborwankar/LearnDataScience

3) scikit-learn tutorial notebooks by Jake VanderPlas
https://github.com/jakevdp/sklearn_tutorial

4) Lots of deep learning notebooks from the book "Deep Learning with Python" by François Chollet
https://github.com/fchollet/deep-learning-with-python-notebo...

Bonus) Jupyter notebook on AWS tutorial (when your local computer just won't handle your notebook requirements):
http://efavdb.com/deep-learning-with-jupyter-on-aws/

Please share your jupyter notebook recommendations.

I wholeheartedly second "Deep Learning with Python" by François Chollet!

It's an excellent 'zero-to-hero' text for understanding deep neural networks, some common architectures, and the code (and theory) to get them to work.

One thing missing is how to prepare data for deep learning -- but that's just standard ETL you learn elsewhere.

Look through youtube for university lectures, like these ones
https://www.youtube.com/playlist?list=PL_Ig1a5kxu57NQ50jSuf0...

Most intro classes just require familiarity with basic calculus (differentiation, chain rule), linear algebra and basic probability all of which you can just lookup directly on https://www.expii.com for a short tutorial. Toolkits are usually in Python or Lua, plus the numerous textbooks 'Deep learning with python' that are around and specific DL books such as http://www.deeplearningbook.org/.

Afterwards look around for Adversarial Learning, like detecting perturbations that force mis-classification and other attacks described in papers by Carlini and Wagner. Currently there isn't a perfect defense developed for all of these attacks, except robust optimization that provably defend some of them. Attacks are an interesting area in DL you can get into since we don't have access to large resources and can only do DL on a small scale (in my case anyway).

For a very practical introduction to deep learning with Keras, I recommend Deep Learning with Python by François Chollet, https://www.manning.com/books/deep-learning-with-python.

For a general context, my post "Learning Deep Learning with Keras" https://p.migdal.pl/2017/04/30/teaching-deep-learning.html or for a practical quick start - "Starting deep learning hands-on: image classification on CIFAR-10" https://deepsense.ai/deep-learning-hands-on-image-classifica....

I wrote a piece on that in: https://github.com/stared/thinking-in-tensors-writing-in-pyt...

> 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 struggled to find something in the middle ground - showing mathematical foundations of deep learning, step by step, at the same time translating it into code. The closest example is CS231n: Convolutional Neural Networks for Visual Recognition (which is, IMHO, a masterpiece). Though, I believe that instead of using NumPy we can use PyTorch, giving a smooth transition between mathematical ideas and a practical, working code.

Not a book per se, but better than any other.

I am in the process of writing "Thinking in Tensors, Writing in PyTorch" (with an idea of showing maths, code, fundamentals or practical examples) but it is a slow process. It's a collaborative, open-source, repo - so open for collaborators and contributors. :)

I second Designing Data-Intensive Applications.

Deep Learning with Python by François Chollet I think works as an audiobook as well.

I am a big non-fiction audio book fan and so much depends on the voice actor. I bad read can ruin the best content while Robertson Dean made Alan Greenspan's The Age of Turbulence into an enthralling adventure story.