Deep Learning Bibliography

If you are interested in finding some textbooks either as an accompaniment to this course or as a way to dig deeper, here are some suggestions:

François Chollet’s Deep Learning with Python is a good beginner-level book, written by the author of Keras. It’s available directly from Manning, from Amazon, and elsewhere.

Goodfellow, Bengio, & Courville’s Deep Learning is a bit more advanced and theoretical. It’s full of math, if you’re interested in deep learning from first principles. It’s available on Amazon, etc. and can be read online.

Somewhat more general (ML rather than DL) is The Mechanics of Machine Learning by Terence Parr and Jeremy Howard. This is a “book in preparation”.

Michael Nielsen’s book Neural Networks and Deep Learning is also available online and is free (voluntary donation requested).

Aurélien Géron: O’Reilly 2019 Hands-On Machine Learning with Scikit-Learn, Keras and Tensorflow (2nd ed)

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I’d also suggest the book “Machine Learning Yearning” by Prof Ng.
Here is a link I found with a search.

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I have seen many people asking about how to find the right Neural Network architecture. Apart from re-using well known architectures or trial-error for new problems, there is a technique called Neural Architecture Search (NAS), which together with several other methods compose the so-called “Automated Machine Learning”. There is an excellent book from Prof. Dr. Hutter covering the state of the art on this matter for free here .

Or you can just watch this talk I moderated some weeks ago from a researcher at Bosch about Neural Architecture Search here

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@paulinpaloalto
How about Prof. Tom Mitchell’s Machine Learning book?
It is a bit more theoretical than Ian Goodfellows book but it goes more in depth as well!

I like Francias’s book as it has a hands-on experience with codes to see keras in action!

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Dive into Deep Learning is a great book to use as a reference. It’s online and interactive with many practical examples implemented with NumPy/MXNet , PyTorch , and TensorFlow

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In addition to the bibliography mentioned before, I would also recommend the Two Minute Papers YouTube channel.
It has brief but extremely entertaining videos explaining the latest I.A. developments. They give a general idea of what the latest papers want to achieve.
I’ve used the channel, for example, to search for the different algorithms to perform Artistic Style transfer when developing a personal PoC.

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One book, which has really stuck with me other than Ian Goodfellow and Tom Mitchel’s book is: “Python Machine Learning by Sebastian Raschka”. It was delight to worth through that book years ago as first edition came out.

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Thanks a lot for all theese input, lots to read :smile:!!
My little piece of contribution: @paulinpaloalto already mentioned François Chollet’s book, I see that the second edition is progressing and available through the Manning Meap program.
Available here Deep Learning with Python, Second Edition

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This would be my two cents :blush:

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I recommend the book from Ian Goodfellow et Al. about Deep Learning, including a chapter about Deep Learning Research. You can find it here.

Happy learning

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I would like to recommend a non-technical book on the recent history of deep learning: Genius Makers. Very entertaining read :nerd_face:

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The Second edition of Chollet’s Deep Learning with Python seems to be progressing well. I’ve just seen the latest refresh on the Manning site, and there are several new chapters. Manning forecast at least one more update before this gets published.

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just howww…tysm…i was wandering on the site.

Last update from Chollet´s book is already available at Manning´s site. (June 2021). The book is complete, I wonder when I will receive the paperback copy I bought one year ago.

Regards.

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Also mention that thanks to Géron´s book I´ve been able to deal with the problems in the last course on Sequence Models. This is a great book also written in a funny style. Moreover the exercises put you in a situation to think hard about how to solve them.

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Hi,

Just sharing this interesting blog about different NN concepts. For instance this is a deep explanation about LSTM networks

http://colah.github.io/posts/2015-08-Understanding-LSTMs/

Best,

Rosa

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Another nice finding about Deep Learning and its Applications

Happy reading

Rosa

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Hello, are the book list still valid nowadays ?
Any new/updated books to suggest ?