I'm not an expert in machine learning. Is there any textbook (with a decent amount of mathematical rigor) that cover the subjects neural network / convolutional neural network / GAN network?  I've the Mitchell - Machine Learning but it is a bit old and it doesn't cover CNN and GAN.  Even a list would be fine so I can check by myself which one is more suitable.
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I want to learn Neural Networks.  I am a Computational Linguist. I know statistical machine learning approaches and can code in Python.   I am looking to start with its concepts, and know one or two popular models which may be useful from a Computational Linguistics perspective.  I browsed the web for reference and found a few books and materials.   Ripley, Brian D. (1996) Pattern Recognition and Neural Networks, Cambridge Bishop, C.M. (1995) Neural Networks for Pattern Recognition, Oxford: Oxford University Press. some links, like ,  (University of Toronto Psychology Department),  (University of Wisconsin Computer Science) and  (Facebook Research).   Coursera courses are generally nice, if anyone knows anything relevant from them. I prefer materials with lucid language and ample examples.
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I know this is a subjective question, but  I need some opinions on a very good book for learning differential equations.   Ideally it should have a variety of problems with worked solutions and be easy to read.   Thanks
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I want to self-study some abstract algebra and cryptography during the summer, so what are some of books that are suitable for self-study? I have very limited background in algebra and none in cryptography, and like to learn by examples. Also, please let me know if there are any books on these subjects with solutions to most of the exercises, so I can check on myself. (I saw 's post about good algebra books, but I would like to focus more on those with good exercises and detailed solutions.)  All suggestions are welcome! Thank you!!
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The question is simple: is there any advantage in using sigmoid function in a convolutional neural network? Because every website that talks about CNN uses Relu function.
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I haven't seen the question stated precisely in these terms, and this is why I make a new question.  What I am interested in knowing is not the definition of a neural network, but understanding the actual difference with a deep neural network.  For more context: I know what a neural network is and how backpropagation works. I know that a DNN must have multiple hidden layers. However, 10 years ago in class I learned that having several layers or one layer (not counting the input and output layers) was equivalent in terms of the functions a neural network is able to represent (see Cybenko's ), and that having more layers made it more complex to analyse without gain in performance. Obviously, that is not the case anymore.  I suppose, maybe wrongly, that the differences are in terms of training algorithm and properties rather than structure, and therefore I would really appreciate if the answer could underline the reasons that made the move to DNN possible (e.g. mathematical proof or randomly playing with networks?) and desirable (e.g. speed of convergence?)
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What is the difference between data mining, statistics, machine learning and AI?  Would it be accurate to say that they are 4 fields attempting to solve very similar problems but with different approaches? What exactly do they have in common and where do they differ? If there is some kind of hierarchy between them, what would it be?  Similar questions have been asked previously but I still don't get it:
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I am looking for suggestions, it has to be a self study book and should be able to relate to applications to real world problems. If it is more computer science oriented , that would be great.
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What is your recommendation for an in-depth introductory combinatoric book? A book that doesn't just tell you about the multiplication principle, but rather shows the whole logic behind the questions with full proofs. The book should be for a first-year-student in college. Do you know a good book on the subject?  Thanks.
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Does it make any sense to have two (or more) neurons in a neural network with the same weights? (intuitively it makes no sense, since all the neurons would behave the same way).   Please consider both the input and hidden layers. What if the weights are equal 0?
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I'm interested in getting some books about multivariate analysis, and need your recommendations. Free books are always welcome, but if you know about some great non-free MVA book, please, state it.
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Does anyone have a recommendation for a book to use for the self study of real analysis? Several years ago when I completed about half a semester of Real Analysis I, the instructor used "Introduction to Analysis" by Gaughan.   While it's a good book, I'm not sure it's suited for self study by itself.  I know it's a rigorous subject, but I'd like to try and find something that "dumbs down" the material a bit, then between the two books I might be able to make some headway.
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I was wondering if there were any machine learning techniques (unsupervised) for modelling longitudinal data? I've always used mixed effects models (mostly non-linear) but I was wondering if there are any other ways of doing this (using machine learning).  By machine learning, I mean random forest, classification/clustering, decision trees and even deep learning, etc.
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I know that a neural-network architecture is mostly based on the problem itself and the types of input/output, but still - there's always a "square one" when starting to build one. So my question is - given a input dataset of MxN (M  is the number of records, N is the number of features) and a C possible output classes - is there a thumb-rule to how many layers/units should we start with?
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Does anyone have any suggestions for abstract algebra books particularly suited to self-study?     Here is some background and motivation, if it's helpful.  I'm currently a junior in high school, but I have some familiarity with groups, rings, polynomials and fields, as I went through Fraleigh's book. I liked its writing style, but many of the problems were not helpful. I would like to get better at algebra with some other book, particularly one that's aimed at someone who already has a little familiarity with the objects, but is by no means completely comfortable.  One of my longer-term goals is to learn a bit of algebraic number theory, as I've only been studying elementary number theory. That's why I feel it  good to learn more algebra first. (I'd also be interested in suggestions of other good subjects to learn prior to tackling algebraic number theory.)  I was considering using one of Lang's books, since they seem pretty universal, but I've heard rumors that they are very terse and not good for self-learning.
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I am about to go study for my masters in machine learning, data mining and high performance computing, but have recently become very interested in cryptography after taking Dan Boneh's Cryptography course on coursera.com.   I was wondering if there are any practical applications for machine learning in cryptography? A quick google search didn't seem to reveal much for me so i was wondering if someone here knows something.  My first impression would be that machine learning techniques could be used to learn the inverse function for some cipher and passphrase given enough plaintext/ciphertext pairs, but i am not sure how capable things like neural networks are at learning functions which are not linearly separable.
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Can a generative adversarial network (GAN) be used for data augmentation (i.e. to generate synthetic examples that are added to a dataset)? Would it have any impact on the performance of a model trained on the augmented dataset?
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Is there a Stack Exchange site for machine learning questions?