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diff --git a/ml/dlib/docs/docs/books.xml b/ml/dlib/docs/docs/books.xml new file mode 100644 index 000000000..cda81bd86 --- /dev/null +++ b/ml/dlib/docs/docs/books.xml @@ -0,0 +1,306 @@ +<?xml version="1.0" encoding="ISO-8859-1"?> +<?xml-stylesheet type="text/xsl" href="stylesheet.xsl"?> + +<doc> + <title>Suggested Books</title> + + + + <!-- ************************************************************************* --> + + <body> + + <p> + One of the major goals of dlib is to have documentation that enables + someone to easily make use of its various components. Ideally, + you would read a short description of something, understand it immediately, + and begin using it in your application without any difficulty. Obviously, this + depends partly on the background of the user. For example, if you have + never written C++ software before then it probably isn't going to be this easy. + </p> + <p> + This page is meant to complement the main library documentation by providing + references to books, along with my commentary, which explain most of + the background material needed to understand the various parts of the library. + In most cases these are the books I learned from during the process + of creating dlib. As always, if you disagree with anything or think I have left out + an important text then shoot me an <a href="mailto:davis@dlib.net">email</a>. + </p> + <br/><br/> + + + <h2>General Programming</h2> + <ul> + <h3>C++</h3> + <ul> + <li> <i>Programming: Principles and Practice Using C++</i> by Bjarne Stroustrup + <ul> This is the sort of book you would use in a freshman introduction-to-programming class. + So if you are just beginning to study programming and are interested in C++ then I think + it is probably safe to say this is one of the best books you could read. </ul> <br/> + </li> + <li> <i>Accelerated C++: Practical Programming by Example</i> by Andrew Koenig and Barbara E. Moo + <ul> If you are new to C++ but already know how to program then this is a great book. It's also + about one fourth the size of the Stroustrup book. </ul> <br/> + </li> + <li> <i>Effective C++: 55 Specific Ways to Improve Your Programs and Designs</i> (3rd Edition) by Scott Meyers + <ul> This is a great intermediate level C++ book. Most people have heard the jokes about + how easy it is to shoot yourself in the foot with C++. This book explains many things you + need to know about the language to avoid doing so on a regular basis. So if you are + writing C++ software then this is a must-read. I would even claim that + you are a danger to the C++ software you touch unless you know what is in this book. + I'm not kidding. Finally, the book isn't just about the quirks of C++. It also discusses many general + software engineering ideas which have wide applicability. So in this + respect it is a great book for any software developer to read. + </ul><br/> + </li> + <li> <i>More Effective C++: 35 New Ways to Improve Your Programs and Designs</i> by Scott Meyers + <ul> Consider this an expansion to Effective C++. If you are going to read the above + book then you would almost certainly benefit from reading this one as well. + </ul><br/> + </li> + <li> <i>The C++ Standard Library: A Tutorial and Reference</i> by Nicolai M. Josuttis + <ul> If you are going to buy a reference book on the C++ standard library then this + is the one to get. I think you + will find it is better than any of the available online references. So if you find + yourself frustrated with the online resources, then this is the book for you. + </ul><br/> + </li> + <li> <a href="http://www.cplusplus.com/reference/">Online C++ Standard Library Reference</a> + <ul> What I said aside, this is a good online reference. I often find myself referring to it + when I do not have the Josuttis book on hand. + </ul><br/> + </li> + </ul> + + + <h3>Multithreading</h3> + <ul> + <li> <i>Programming with POSIX Threads</i> by David R. Butenhof + <ul> When I was an undergrad, this book was my main resource for learning about multithreading. + It was enjoyable to read, as are all the books on this list, and covered everything + in great depth without becoming overbearing. Also, despite what the title may suggest, + this book is useful for understanding multithreading broadly, not just multithreading + on POSIX systems. + </ul><br/> + </li> + </ul> + + <h3>Network Programming</h3> + <ul> + <li> <i>Unix Network Programming, Volume 1: The Sockets Networking API</i> (3rd Edition) + by W. Richard Stevens + <ul> A lot of people call this book the network programming Bible and + this praise is well deserved. If you want a deep understanding of how computer networks + function, including the Internet, then this is the book to read. As with + the Butenhof book above, this is an excellent choice even for people who do not + intend to write software for Unix systems. + </ul><br/> + </li> + </ul> + + <h3>WIN32 Programming</h3> + It has been a long time since I needed to refer to these two books. However, + they contained information I couldn't find elsewhere no matter + how hard I looked. So I recommend them in case you need to create or understand + some low level win32 code. + <br/> + <br/> + <ul> + <li> <i>Win32 Programming</i> by Brent E. Rector and Joseph M. Newcomer </li> + <li> <i>Programming Windows</i> by Charles Petzold </li> + <li> <a href="http://msdn.microsoft.com/en-us/library/default.aspx">MSDN Library</a> + <ul> This is Microsoft's online reference documentation. It is very large and sometimes + confusing. But at the end of the day you should be able to find the documentation + for just about every function in the entire Windows API. + </ul><br/> + </li> + </ul> + </ul> + + + + + + <h2>Computer Science: Algorithms and Data Structures</h2> + <ul> + <li> <i>Introduction to Algorithms</i> by Cormen, Leiserson, Rivest and Stein + <ul> You should get this book if you are looking for a good discussion of the classic computer science + algorithms and data structures (e.g. most of the components on the <a href="containers.html">containers</a> + page). + </ul><br/> + </li> + <li> <i>Algorithms in C++, Parts 1-4: Fundamentals, Data Structure, Sorting, Searching</i> + (3rd Edition) by Robert Sedgewick + <ul> This is another good algorithms book. If you are going to get only one book on this + subject then get the one above. However, when I was learning about these topics I + used both these books and on many occasions I found it helpful to read the description + of an algorithm or data structure in both. Where one description was a little vague or + confusing the other generally filled in the gaps. + </ul><br/> + </li> + </ul> + + + + <h2>Lossless Data Compression</h2> + <ul> + <li> <i>Text Compression</i> by Bell, Cleary, and Witten + <ul> When I was studying data compression this was my most useful + resource. If you are looking to understand how lossless data compression + algorithms work then this is the book you want. It is completely self-contained + and an absolute joy to read. Note that contrary to one of the reviews on + amazon.com, the book <i>Managing Gigabytes</i> is not the second edition of this book; + if this topic interests you then be sure you get the 318 page + book published in 1990. + </ul><br/> + </li> + </ul> + + + + <h2>General Math</h2> + + <ul> + <li> <i>Linear Algebra Done Right</i> by Sheldon Jay Axler + <ul> If a matrix seems like an arbitrary grid of numbers or you find that + you are confused by vectors, matrices, and the various things + that get done with them then this book will change your whole view of this subject. + It doesn't teach you any algorithms. Instead, it will give you a general + framework in which to think about all this stuff. Once you have that down + everything else will start to make a lot more sense. If all goes well + you will even start to agree with the following: linear algebra is beautiful. :) + </ul><br/> + </li> + <li> <i>Numerical Linear Algebra</i> by Trefethen and Bau + <ul> While <i>Linear Algebra Done Right</i> is fairly abstract, this book by + Trefethen and Bau will + explain some of the actual algorithms that are often used. + This is a great second book if you find that you want to know more about + the SVD, LU decomposition, or various other algorithms involving linear algebra. + </ul><br/> + </li> + <li> <i>Calculus: Single and Multivariable</i> by Hughes-Hallett, Gleason, and McCallum + <ul> + Some of the books below will require and understanding of basic calculus. So + I'm recommending this book. It was the book I used as an undergrad and I + remember it being alright. That isn't exactly a glowing review so if you + are really considering buying a calculus book you may want to check out + other reviews before picking this one. + </ul><br/> + </li> + <li> <i>Introduction to Real Analysis</i> (third edition) by Bartle and Sherbert + <ul> At some level real analysis is like a really rigorous repeat of calculus. + So if you already have an undergraduate education in calculus and + you are reading things that seem reminiscent of calculus but involve + stuff you haven't seen before (e.g. sup, inf, "sets of numbers", sequences of points) + then you may be in need of a real analysis book. This one is quite good and should + be accessible to someone with the usual undergraduate computer science math background. + </ul><br/> + </li> + </ul> + + + + + + <h2>Optimization</h2> + + The subject of linear algebra is fundamental to optimization. So you must be familiar + with the contents of a book like <i>Linear Algebra Done Right</i> if you are going to study + this area. You will also need to know how to find the derivative of a function and + understand what a derivative is all about. So you will need to know a little bit of + calculus. Finally, once in a while you will need to know a little bit about real + analysis. Ultimately, what you need all depends on how deep you want to go. + + <ul> + <li> <i>Practical Methods of Optimization</i> (second edition) by R. Fletcher 1987 + <ul> I love this book. When I got it I literally spent my weekends sitting around + reading it for hours. It is a fascinating and well written introduction to + the subject of optimization. This has been my most valuable resource for + learning the fundamentals of optimization and I cannot recommend it highly enough. + </ul><br/> + </li> + <li> <i>Numerical Optimization</i> by Jorge Nocedal and Stephen Wright 2006 + <ul> This is a more recent text on optimization that is also very good. It + covers many algorithms not covered by the above book. + </ul><br/> + </li> + <li> <i>Introduction to Derivative-Free Optimization</i> by Conn, Scheinberg, and Vicente + <ul> If you want to understand algorithms like <a href="optimization.html#find_min_bobyqa">BOBYQA</a> + then this is a good recent book on the subject. Note that a book like <i>Practical Methods of Optimization</i> + is almost certainly a prerequisite for reading this book. As an aside, BOBYQA is not discussed in this book but + its predecessor, NEWUOA is. + </ul><br/> + </li> + </ul> + + + + + <h2>Machine Learning</h2> + + <ul> + <li> <i>Artificial Intelligence: A Modern Approach </i> (3rd Edition) by Stuart Russell and Peter Norvig + <ul> This book is about the much broader field of AI but it contains an excellent introduction + to machine learning and it also covers other useful topics like <a href="bayes.html">bayesian networks</a>. + Moreover, it is very well written and self-contained. So you don't need any particular + background to be able to learn from it apart from a typical undergraduate background + in computer science. + </ul><br/> + </li> + <li> <i>Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond </i> + by Bernhard Schlkopf and Alexander J. Smola + <ul> Most of the machine learning tools in dlib are implementations of various kernel methods. + So if you want a book that covers this topic in great depth as well as breadth then this is + probably the book for you. The most important prerequisite for this book is linear + algebra. Virtually everything in this book depends on linear algebra in a fundamental way. + <p> + The second important subject is optimization. Whenever you see the text + mention the KKT conditions, duality, "primal variables", or quadratic programming it + is talking about ideas from optimization. A good book which will explain all this to you + is <i>Practical Methods of Optimization</i>. Note that this book calls the KKT conditions + just the "KT" conditions. It is talking about the same thing. Also, duality + is something that comes up a lot in optimization but in the context of machine learning + usually people are talking about a particular form known as the Wolfe Dual. + </p> + It would also be good (but maybe not critical depending on which parts you want to read) to + be familiar with real analysis. + </ul><br/> + </li> + <li> <i>Kernel Methods for Pattern Analysis </i> by John Shawe-Taylor and Nello Cristianini + <ul> This is another good book about kernel methods. If you have to choose between + this book and <i>Learning with Kernels</i> I would go with <i>Learning with Kernels</i>. However, it is + good to have both since reading different presentations of difficult subjects + usually makes learning them easier. + </ul><br/> + </li> + + <li> <i>Structured Prediction and Learning in Computer Vision</i> by Sebastian Nowozin and Christoph H. Lampert 2011 + <ul> If you are looking for a book discussing the background material necessary + for understanding things like the <a href="ml.html#structural_svm_problem">Structural SVM</a> + tools in dlib then this is a good book. It is also available online + in <a href="http://www.nowozin.net/sebastian/papers/nowozin2011structured-tutorial.pdf">PDF form</a>. + </ul><br/> + </li> + + </ul> + + <h2>Image Processing</h2> + <ul> + <li> <i>Digital Image Processing</i> by Rafael C. Gonzalez and Richard E. Woods + <ul> This is a terrific introduction to digital image processing. + By and large this book doesn't require any special prerequisites. Sometimes + calculus shows up, but not too much. + </ul><br/> + </li> + </ul> + + + </body> + + + + <!-- ************************************************************************* --> + +</doc> + |