Well then I'm sorry, cheers and thanks for the link! I'm looking for something to read after "finishing" this one.
It really depends on your comfort and familiarity with the topics. If you've seen analysis before you can probably skip Rudin. If you've seen some functional analysis, you can skip the functional analysis book. Convex Optimization can be read in tandem with ESL, and is probably the most important of the three.
Per my other comment, if your goal is to really understand the material, it's important you understand all the math, at least in terms of reading. Unless you want to do research, you don't need to be able to reproduce all the proofs (to help you gauge your depth of understanding). In terms of bang for your buck, ESL and Convex Optimization are probably the two I'd focus on. Another great book Deep Learning this book is extremely approachable with a modest math background, IMO.
It's only $72 on Amazon. It's mathematical, but without following the Theorem -> Proof style of math writing.
The first 1/3 of the book is a review of Linear Algebra, Probability, Numerical Computing, and Machine Learning.
The middle 1/3 of the book is tried-and-true neural nets (feedforward, convolutional, and recurrent). It also covers optimization and regularization.
The final 1/3 of the book is bleeding edge research (autoencoders, adversarial nets, Boltzmann machines, etc.).
The book does a great job of foreshadowing. In chapters 4-5 it frames problems with the algorithms being covered, and mentions how methods from the final 1/3 of the book are solving them.
https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/
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Te respondi todo viste ;)
Espero que te sirva.
I’d personally recommend Andrew Ng’s deeplearning.ai course if you’re just starting. This will give you practical and guided experience to tensorflow using jupyter notebooks.
If it’s books you really want I found the following of great use in my studies but they are quite theoretical and framework agnostic publications. Will help explain the theory though:
Deep Learning (Adaptive Computation and Machine Learning Series) https://www.amazon.co.uk/dp/0262035618/ref=cm_sw_r_cp_api_i_Hu41Db30AP4D7
Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series) https://www.amazon.co.uk/dp/0262039249/ref=cm_sw_r_cp_api_i_-y41DbTJEBAHX
Pattern Recognition and Machine Learning (Information Science and Statistics) (Information Science and Statistics) https://www.amazon.co.uk/dp/0387310738/ref=cm_sw_r_cp_api_i_dv41DbTXKKSV0
Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) https://www.amazon.co.uk/dp/B00AF1AYTQ/ref=cm_sw_r_cp_api_i_vx41DbHVQEAW1
Amazon, who's not always reliable, claims December 9th : https://smile.amazon.com/dp/0262035618/
That is indeed more of a surface-level book to provide you with some context (it's definitely not a book to learn ML from). The classical book many people learn ML from is this - https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618
Depends on so many factors. Are you just looking to write deep learning algorithms or are you actually interested in research + theory? What sort of math are you comfortable with? Can you read & write proofs?
You're probably going to see the book "Deep Learning" by Goodfellow et al mentioned here or elsewhere. Keep in mind it's pretty math-heavy and probably won't really help you write code. You'll need to know calculus and at least linear algebra. That being said it's a great book if you're looking for theory and math.
Honestly, if you're not comfortable with multivariable calculus and linear algebra I wouldn't suggest you start learning deep learning at all until those are familiar to you. It would also behoove you to have a solid foundation in mathematical statistics and probability.
Assuming you have the necessary background in mathematics I would recommend the following:
Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig will provide a broad overview of the theory and practice of artificial intelligence generally.
The Elements of Statistical Learning would be a good place to go from there.
The aforementioned Deep Learning book by Goodfellow et al.
After those look for something practical. Someone here mentioned a book they wrote. Always a good idea to support the Reddit community. There's also:
I really like Deep Learning by Ian Goodfellow, et al. You can but it from Amazon at https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/ref=sr_1_1?ie=UTF8&qid=1472485235&sr=8-1&keywords=deep+learning+book. If you are a little cash strapped, there is an html version at http://www.deeplearningbook.org/. Of course, this book is specifically focused on neural networks as opposed to ML in general.
free online: https://www.deeplearningbook.org/
or purchase on amazon: https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/
or elsewhere: https://en.wikipedia.org/wiki/Special:BookSources?isbn=978-0262035613
This one has math fundamentals and in fact every model has underlying math formulas and in accompanied with code for Mxnet, Pytorch and Tensorflow. I like the way the authors combined theory with practice, you are getting best of both worlds. There are also notebooks on every model. http://d2l.ai/
Another good math heavy read is Deep Learning. From Goodfellow et al. https://www.amazon.de/dp/0262035618/ref=as_li_tl?ie=UTF8&linkCode=gs2&linkId=00701393c949f16bfd3a89d9c3240b35&creativeASIN=0262035618&tag=petacrunch07-21&creative=9325&camp=1789
This is a great book that takes you from chapter 1 in linear algebra and goes into machine learning with neural networks.
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https://www.amazon.com/gp/product/0262035618/ref=ppx_yo_dt_b_asin_title_o08_s00?ie=UTF8&psc=1
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The authors also have a website with some of the material.
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I would start with reading.
For Neural Networks, I'd do:
Deep Learning (Adaptive Computation and Machine Learning series) https://www.amazon.com/dp/0262035618/ref=cm_sw_r_cp_apa_i_nC11CbNXV2WRE
Neural Networks and Learning Machines (3rd Edition) https://www.amazon.com/dp/0131471392/ref=cm_sw_r_cp_apa_i_OB11Cb24V2TBE
For overview with NN, Fuzzy Logic Systems, and Evolutionary Algorithms, I recommend:
Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation (IEEE Press Series on Computational Intelligence) https://www.amazon.com/dp/1119214343/ref=cm_sw_r_cp_apa_i_zD11CbWRS95XY
Simon Goodfellow's book is a really good resource for deep learning.
https://www.amazon.com/dp/0262035618/ref=cm_sw_r_cp_awdb_t1_rVmvBbJ0DGFKH
And Data Mining gives a good overview of a variety of algorithms.
https://www.amazon.com/dp/0128042915/ref=cm_sw_r_cp_awdb_t1_0XmvBbCAH9Q77
I think the name of the book is literally Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.