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- Suggested Books</title><script type="text/javascript" src="dlib.js"></script><link rel="stylesheet" type="text/css" href="dlib.css"></head><body><a name="top"></a><div id="page_header"><a href="http://dlib.net"><img src="dlib-logo.png"></a></div><div id="top_content"><div id="main_menu" class="menu"><div class="menu_top"><b>The Library</b><ul class="tree"><li><a href="algorithms.html" class="menu">Algorithms</a></li><li><a href="api.html" class="menu">API Wrappers</a></li><li><a href="bayes.html" class="menu">Bayesian Nets</a></li><li><a href="compression.html" class="menu">Compression</a></li><li><a href="containers.html" class="menu">Containers</a></li><li><a href="graph_tools.html" class="menu">Graph Tools</a></li><li><a href="imaging.html" class="menu">Image Processing</a></li><li><a href="linear_algebra.html" class="menu">Linear Algebra</a></li><li><a href="ml.html" class="menu">Machine Learning</a></li><li><a href="metaprogramming.html" 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Last Modified:<br>Sep 13, 2015</div></div><div id="main_text"><div id="main_text_title">Suggested Books</div><div id="main_text_body"><p> |
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One of the major goals of dlib is to have documentation that enables |
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someone to easily make use of its various components. Ideally, |
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you would read a short description of something, understand it immediately, |
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and begin using it in your application without any difficulty. Obviously, this |
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depends partly on the background of the user. For example, if you have |
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never written C++ software before then it probably isn't going to be this easy. |
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</p><p> |
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This page is meant to complement the main library documentation by providing |
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references to books, along with my commentary, which explain most of |
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the background material needed to understand the various parts of the library. |
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In most cases these are the books I learned from during the process |
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of creating dlib. As always, if you disagree with anything or think I have left out |
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an important text then shoot me an <a href="mailto:[email protected]">email</a>. |
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</p><br><br><a name="General%20Programming"></a><h2>General Programming</h2><ul><a name="C++"></a><h3>C++</h3><ul><li><i>Programming: Principles and Practice Using C++</i> by Bjarne Stroustrup |
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<ul> This is the sort of book you would use in a freshman introduction-to-programming class. |
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So if you are just beginning to study programming and are interested in C++ then I think |
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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 |
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<ul> If you are new to C++ but already know how to program then this is a great book. It's also |
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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 |
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<ul> This is a great intermediate level C++ book. Most people have heard the jokes about |
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how easy it is to shoot yourself in the foot with C++. This book explains many things you |
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need to know about the language to avoid doing so on a regular basis. So if you are |
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writing C++ software then this is a must-read. I would even claim that |
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you are a danger to the C++ software you touch unless you know what is in this book. |
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I'm not kidding. Finally, the book isn't just about the quirks of C++. It also discusses many general |
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software engineering ideas which have wide applicability. So in this |
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respect it is a great book for any software developer to read. |
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</ul><br></li><li><i>More Effective C++: 35 New Ways to Improve Your Programs and Designs</i> by Scott Meyers |
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<ul> Consider this an expansion to Effective C++. If you are going to read the above |
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book then you would almost certainly benefit from reading this one as well. |
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</ul><br></li><li><i>The C++ Standard Library: A Tutorial and Reference</i> by Nicolai M. Josuttis |
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<ul> If you are going to buy a reference book on the C++ standard library then this |
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is the one to get. I think you |
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will find it is better than any of the available online references. So if you find |
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yourself frustrated with the online resources, then this is the book for you. |
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</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 |
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when I do not have the Josuttis book on hand. |
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</ul><br></li></ul><a name="Multithreading"></a><h3>Multithreading</h3><ul><li><i>Programming with POSIX Threads</i> by David R. Butenhof |
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<ul> When I was an undergrad, this book was my main resource for learning about multithreading. |
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It was enjoyable to read, as are all the books on this list, and covered everything |
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in great depth without becoming overbearing. Also, despite what the title may suggest, |
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this book is useful for understanding multithreading broadly, not just multithreading |
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on POSIX systems. |
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</ul><br></li></ul><a name="Network%20Programming"></a><h3>Network Programming</h3><ul><li><i>Unix Network Programming, Volume 1: The Sockets Networking API</i> (3rd Edition) |
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by W. Richard Stevens |
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<ul> A lot of people call this book the network programming Bible and |
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this praise is well deserved. If you want a deep understanding of how computer networks |
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function, including the Internet, then this is the book to read. As with |
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the Butenhof book above, this is an excellent choice even for people who do not |
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intend to write software for Unix systems. |
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</ul><br></li></ul><a name="WIN32%20Programming"></a><h3>WIN32 Programming</h3> |
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It has been a long time since I needed to refer to these two books. However, |
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they contained information I couldn't find elsewhere no matter |
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how hard I looked. So I recommend them in case you need to create or understand |
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some low level win32 code. |
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<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 |
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confusing. But at the end of the day you should be able to find the documentation |
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for just about every function in the entire Windows API. |
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</ul><br></li></ul></ul><a name="Computer%20Science:%20Algorithms%20and%20Data%20Structures"></a><h2>Computer Science: Algorithms and Data Structures</h2><ul><li><i>Introduction to Algorithms</i> by Cormen, Leiserson, Rivest and Stein |
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<ul> You should get this book if you are looking for a good discussion of the classic computer science |
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algorithms and data structures (e.g. most of the components on the <a href="containers.html">containers</a> |
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page). |
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</ul><br></li><li><i>Algorithms in C++, Parts 1-4: Fundamentals, Data Structure, Sorting, Searching</i> |
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(3rd Edition) by Robert Sedgewick |
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<ul> This is another good algorithms book. If you are going to get only one book on this |
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subject then get the one above. However, when I was learning about these topics I |
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used both these books and on many occasions I found it helpful to read the description |
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of an algorithm or data structure in both. Where one description was a little vague or |
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confusing the other generally filled in the gaps. |
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</ul><br></li></ul><a name="Lossless%20Data%20Compression"></a><h2>Lossless Data Compression</h2><ul><li><i>Text Compression</i> by Bell, Cleary, and Witten |
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<ul> When I was studying data compression this was my most useful |
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resource. If you are looking to understand how lossless data compression |
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algorithms work then this is the book you want. It is completely self-contained |
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and an absolute joy to read. Note that contrary to one of the reviews on |
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amazon.com, the book <i>Managing Gigabytes</i> is not the second edition of this book; |
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if this topic interests you then be sure you get the 318 page |
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book published in 1990. |
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</ul><br></li></ul><a name="General%20Math"></a><h2>General Math</h2><ul><li><i>Linear Algebra Done Right</i> by Sheldon Jay Axler |
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<ul> If a matrix seems like an arbitrary grid of numbers or you find that |
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you are confused by vectors, matrices, and the various things |
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that get done with them then this book will change your whole view of this subject. |
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It doesn't teach you any algorithms. Instead, it will give you a general |
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framework in which to think about all this stuff. Once you have that down |
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everything else will start to make a lot more sense. If all goes well |
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you will even start to agree with the following: linear algebra is beautiful. :) |
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</ul><br></li><li><i>Numerical Linear Algebra</i> by Trefethen and Bau |
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<ul> While <i>Linear Algebra Done Right</i> is fairly abstract, this book by |
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Trefethen and Bau will |
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explain some of the actual algorithms that are often used. |
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This is a great second book if you find that you want to know more about |
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the SVD, LU decomposition, or various other algorithms involving linear algebra. |
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</ul><br></li><li><i>Calculus: Single and Multivariable</i> by Hughes-Hallett, Gleason, and McCallum |
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<ul> |
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Some of the books below will require and understanding of basic calculus. So |
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I'm recommending this book. It was the book I used as an undergrad and I |
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remember it being alright. That isn't exactly a glowing review so if you |
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are really considering buying a calculus book you may want to check out |
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other reviews before picking this one. |
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</ul><br></li><li><i>Introduction to Real Analysis</i> (third edition) by Bartle and Sherbert |
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<ul> At some level real analysis is like a really rigorous repeat of calculus. |
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So if you already have an undergraduate education in calculus and |
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you are reading things that seem reminiscent of calculus but involve |
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stuff you haven't seen before (e.g. sup, inf, "sets of numbers", sequences of points) |
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then you may be in need of a real analysis book. This one is quite good and should |
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be accessible to someone with the usual undergraduate computer science math background. |
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</ul><br></li></ul><a name="Optimization"></a><h2>Optimization</h2> |
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|
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The subject of linear algebra is fundamental to optimization. So you must be familiar |
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with the contents of a book like <i>Linear Algebra Done Right</i> if you are going to study |
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this area. You will also need to know how to find the derivative of a function and |
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understand what a derivative is all about. So you will need to know a little bit of |
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calculus. Finally, once in a while you will need to know a little bit about real |
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analysis. Ultimately, what you need all depends on how deep you want to go. |
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|
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<ul><li><i>Practical Methods of Optimization</i> (second edition) by R. Fletcher 1987 |
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<ul> I love this book. When I got it I literally spent my weekends sitting around |
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reading it for hours. It is a fascinating and well written introduction to |
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the subject of optimization. This has been my most valuable resource for |
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learning the fundamentals of optimization and I cannot recommend it highly enough. |
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</ul><br></li><li><i>Numerical Optimization</i> by Jorge Nocedal and Stephen Wright 2006 |
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<ul> This is a more recent text on optimization that is also very good. It |
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covers many algorithms not covered by the above book. |
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</ul><br></li><li><i>Introduction to Derivative-Free Optimization</i> by Conn, Scheinberg, and Vicente |
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<ul> If you want to understand algorithms like <a href="optimization.html#find_min_bobyqa">BOBYQA</a> |
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then this is a good recent book on the subject. Note that a book like <i>Practical Methods of Optimization</i> |
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is almost certainly a prerequisite for reading this book. As an aside, BOBYQA is not discussed in this book but |
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its predecessor, NEWUOA is. |
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</ul><br></li></ul><a name="Machine%20Learning"></a><h2>Machine Learning</h2><ul><li><i>Artificial Intelligence: A Modern Approach </i> (3rd Edition) by Stuart Russell and Peter Norvig |
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<ul> This book is about the much broader field of AI but it contains an excellent introduction |
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to machine learning and it also covers other useful topics like <a href="bayes.html">bayesian networks</a>. |
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Moreover, it is very well written and self-contained. So you don't need any particular |
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background to be able to learn from it apart from a typical undergraduate background |
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in computer science. |
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</ul><br></li><li><i>Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond </i> |
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by Bernhard Schlkopf and Alexander J. Smola |
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<ul> Most of the machine learning tools in dlib are implementations of various kernel methods. |
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So if you want a book that covers this topic in great depth as well as breadth then this is |
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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. |
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<p> |
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The second important subject is optimization. Whenever you see the text |
|
mention the KKT conditions, duality, "primal variables", or quadratic programming it |
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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. |
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</p> |
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It would also be good (but maybe not critical depending on which parts you want to read) to |
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be familiar with real analysis. |
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</ul><br></li><li><i>Kernel Methods for Pattern Analysis </i> by John Shawe-Taylor and Nello Cristianini |
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<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 |
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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 |
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in <a href="http://www.nowozin.net/sebastian/papers/nowozin2011structured-tutorial.pdf">PDF form</a>. |
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</ul><br></li></ul><a name="Image%20Processing"></a><h2>Image Processing</h2><ul><li><i>Digital Image Processing</i> by Rafael C. Gonzalez and Richard E. Woods |
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<ul> This is a terrific introduction to digital image processing. |
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By and large this book doesn't require any special prerequisites. Sometimes |
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calculus shows up, but not too much. |
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</ul><br></li></ul></div></div></div><div id="bottom_content"></div></body></html> |
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