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CUDA Programming: A Developer's Guide toParallel Computing with GPUsShane Cook This book provides an in-depth coverage of important aspectsrelated to CUDA programming -- a programming technique that canunleash the unparalleled power of GPU

   CUDA Programming: A Developer's Guide toParallel Computing with GPUsShane Cook

This book provides an in-depth coverage of important aspectsrelated to CUDA programming -- a programming technique that canunleash the unparalleled power of GPU computation. With CUDA and anaffordable GPU card, you can run your data analysis program in thematter of minutes which may otherwise require multiple servers torun for hours.

This book covers important topics that you should know indeveloping high performance computing programs. Particularly, itintroduces SIMD, memory hierarchies, OpenMP, and MPI. With theseknowledges in mind, you understand what are the factors that mightinfluence the run-time performance of your codes.

Introduction to High PerformanceComputing for Scientists and EngineersGeorg Hager and Gerhard Wellein

Recent trends of hardware advancement has switched from increasingCPU frequencies to increasing the nu我没有晓得机械进建册本mber of cores. A significantimplication of this change is that "free lunch has come to an end"-- you have to explicitly parallelize your codes in order tobenefit from the latest progress on CPU/GPUs. This book summarizescommon patterns used in parallel programming, such as mapping,reduction, and pipelining -- all are very useful in writingparallel codes.

Structured Parallel Programming:Patterns for Efficient ComputationMichael McCool, James Reinders, and Arch Robison

Textbooks on C++, Java, or other languages typically use toyexamples您晓得asmartingales (animals, students, etc) to illustrate the concept of OOP.This way, however, does not reflect the full strength of objectoriented programming. This book, which has been widely acknowledgedas a classic in software en机械进建册本gineering, shows you, via compellingexamples distilled from real world projects, how specific OOPpatterns can vastly improve your code's reusability andextensibility.

Design Patterns: Elements of ReusableObject-Oriented SoftwareErich Gamma, Richard Helm, Ralph Johnson, and John Vlissides

If you know nothing about algorithms, you never understand computerscience. This is book is definitely a classic on algorithms anddata structures that everyone who is serious about computer sciencemust read. This contents of this book ranges from elementary topicssuch as classic sorting algorithms and hash table to advancedtopics such as maximum flow, linear programming, and computationalgeometry. It is a book for everyone. Everytime I read it, I learnedsomething new.

Introduction to Algorithms (2nd/3rdEdition)Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, andClifford Stein

Like it or hate it, meta-programming has played an increasing机械进建册本lyimportant role in modern C++ development. If you asked what is thekey aspects that distinguishes C++ from all other languages, Iwould say it is the unparalleled generic programming capabilitybased on C++ templates. This book summarizes the latest advancementof metaprogramming in the past decade. I believe it will take theplace of Loki's "Modern C++ Design" to become the bible for C++meta-programming.

Advanced C++ MetaprogrammingDavide Di Gennaro

The Effective C++ series by Scott Meyers is a must for anyone whois serious about C++ programming. The items (rules) listed in thisbook conveys the author's deep understanding of both C++ itself andmodern software engineering principles. This edition reflectslatest updates in C++ development, including generic programmingthe use of TR1 library.

Effective C++: 55 Specific Ways to念晓得Improve Your Programs and Designs (3rd Edition)Scott Meyers

While it is kind of old (written in 2000), I still recommend thisbook to all beginners to learn C++. The thoughts underlyingobject-oriented programming is very clearly explained. It alsoprovides a comprehensive coverage of C++ in a well-tuned pace.

Thinking in C++: Introduction toStandard C++ (2nd Edition)Bruce Eckel

Timeless classic that must be read by all computer science majors.While some topics and the use of Scheme as the teaching languageseems odd at first glance, the presentation of fundamental conceptssuch as abstraction, recursion, and modularity is so beautiful andinsightful that you would never experienced elsewhere.

ProgrammingStructure and Interpretation of ComputerPrograms (2nd Edition)Har机械进建册本old Abelson, Gerald Jay Sussman, and Julie Sussman

If you are interested in Bayesian nonparametrics, this is the bookthat you should definitely check out. This manuscript provides anunparalleled introduction to random point processes, includingPoisson and Cox process模板工程安全技术规范闭于机械进建册本es, and their deep theoretical connectionswith complete randomness.

Poisson Processes (Oxford Studies inProbability)J. F. C. Kingman

A classic textbook on stochastic process which I think areparticularly suitable for beginners without much background onmeasure theory. It provides a complete coverage of many importantstochastic processes in an intuitive way. Its development of Markovprocesses and renewal processes is enlightening.

A First Course in Stochastic Processes(2nd Edition)Samuel Karlin, and Howard M. Taylor

This is a complete coverage of modern probability theory -- notonly including traditional topics, such as measure theory,independence, and convergence theorems, but also introducing topicsthat are typically in textbooks on stochastic processes, such asMartingales, Markov chains, and Brownian motion, Poisson processes,and Stochastic differential equations. It is reco机械进建册本mmended as themain textbook on probability theory.

Probability Theory: A ComprehensiveCourse (Universitext)Achim Klenke

It is a modern treatment of this classical theory, which emphasizesthe connections with other mathematical subjects -- for example,random walks and electrical networks. I found some messagesconveyed by this book is enlightening for m看着suchy research on machinelearning methods.

Modern Graph TheoryBela Bollobas

This is the book that I used to learn differential geometry and Liegroup theory. It provides a detailed 机械进建册本introduction to basics ofmodern differential geometry -- manifolds, tangent spaces, andvector bundles. The connections between manifold theory and Liegroup theory is also clearly explained. It also covers De RhamCohomology and Lie algebra, where audience is invited to discoverthe beauty by linking geometry with algebra.

Introduction to Smooth ManifoldsJohn M. Lee

A thorough treatment of nonlinear optimization. It coversgradient-based techniques, Lagrange multiplier theory, and convexprogramming. Part of this book overlaps with Boyd's. Overall, itgoes deeper and takes more efforts to read.

Nonlinear Programming (2nd Edition)Dimitri P. Bersekas

A classic on convex optimization. Everyone that I knew who had readthis book liked it. The presentation style is very comfortable andinspiring, and it assumes only minimal比照1下机械进建册本 prerequisite on linearalgebra and calculus. Strongly recommended for any beginners onoptimization. Note: the pdf of this book is freely available on theProf. Boyd's website.

Convex OptimizationStephen Boyd, and Lieven Vandenberghe

This is a dense text that combines Real analysis and modernprobability theory in 500+ pages. What I like about this book isits treatment that emphasizes the interplay between real analysisand probability theory. Also the exposition of measure theory basedon semi-rings gives a deep insight of the algebraic structure ofmeasures.

Real Analysis and Probability (CambridgeStudies in Advanced Mathematics)R. M. Dudley

It is a very well written book on 机械进建册本functional analysis that I wouldlike to recommend to every one who would like to study this subjectfor the first time. Starting from *** notions such as metricsand norms, the book gradually unfolds the beauty of functionalanalysis, exposing important topics including Banach spaces,Hilbert spaces, and spectral theory with a reasonable depth andbreadth. Most important concepts needed in machine learning arecovered by this book. The exercises are of great help to reinforceyour understanding.

Introductory Functional Analysis withApplicationsErwin Kreyszig

A classic on topology for beginners. It provides a clearintroduction of important concepts in general topology, such ascontinuity, connectedness, compact机械进建册本ness, and metric spaces, whichare the fundamentals that you have to grasped before embarking onmore advanced subjects such as real analysis.

MathematicsTopology (2nd Edition)James Munkres

A comprehensive and in-depth treatment of kernel methods andsupport vector machine. It not only clearly develops themathematical foundation, namely the reproducing kernel Hilbertspace, but also gives a lot of practical guidance (e.g. how tochoose or design kernels.)

Learning withKernels: Support Vector Machines, Regularization, Optimization, andBeyondBernhard Schlkopf and Alexander J. Smola

A well written book on pattern recognition for beginners. It事真上such coversbasic topics in this field, including discriminant analysis,decision trees, feature selection, and clustering -- all are basicknowledge that researchers in machine learning or patternrecognition should understand.

Statistical PatternRecognition (2nd/3rd Edition)Andrew R. Webb, and Keith D. Copsey

A short but insightful manuscript that will motivate you to rethinkhow we should face the explosive growth of data in the newcentury.

Big 看着机械进建册本Data: ARevolution That Will Transform How We Live, Work, andThinkViktor Mayer-Schonberger, and Kenneth Cukier

It is a comprehensive and brilliant presentation of three closelyrelated subjects: graphical models, exponential families, andvariational inference. This is the best manuscript that I have everread on this subject. Strongly recommended to everyone interestedin graphical models. The connections between various inferencealgorithms and convex optimization is clearly explained. Note: pdfversion of this book is freely available online.

Graphical Models,Exponential Families, and Variational InferenceMartin J. Wainwright and Michael I. Jordan

A new treatment of classic machine learning topics, such asclassification, regression, and time series analysis from aBayesian perspective. It is a must read for people who intends toperform research on Bayesian learning and probabilisticinference.

MachineLasMartingalesearningPattern Recognitionand Machine LearningChristopher M. Bishop

Here is a list of books which I have read and feel it is worthrecommending to friends who are interested in computer science.


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