/Books

Interesting scientific books

Books

A short list of scientific books that I read or plan to buy.

Books that I read cover to cover

Effective STL, Scott Meyers

The C Programming Language, Brian W. Kernighan and Dennis Ritchie

Introduction to Algorithms, Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein

Modern C++ Design, Andrei Alexandrescu

C++ Concurrency In Action, Anthony Williams

C++ Template Metaprogramming, David Abrahams

From Mathematics to Generic Programming, Alexander A. Stepanov

Exceptional C++, Herb Sutter

Boost C++ Application Development Cookbook

Algorithms, Robert Sedgewick and Kevin Wayne

Elements of Programming, Alexander A. Stepanov

Exceptional C++ Style, Herb Sutter

Design Patterns, Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides and Grady Booch

C++ Coding Standards, Herb Sutter

Javascript: The Good Parts, Douglas Crockford

The Algorithm Design Manual, Steven S. Skiena

Effective Modern C++, Scott Meyers

The C++ Standard Library, Nicolai M. Josuttis

An Introduction to Statistical Learning, Gareth James and Daniela Witten

Imperfect C++, Matthew Wilson

More Exceptional C++, Herb Sutter

A Tour of C++, Bjarne Stroustrup

Cracking the Tech Career, Gayle Laakmann McDowell

Python Machine Learning, Sebastian Raschka

Books that I partially read or browse

Cracking the Coding Interview, Gayle Laakmann McDowell

Introduction to Parallel Computing, Ananth Grama, George Karypis, Vipin Kumar and Anshul Gupta

Real-Time Collision Detection, Christer Ericson

Learning Python, Mark Lutz

Introduction to HPC for Scientists and Engineers, Georg Hager and Gerhard Wellein

Computer Architecture, John L. Hennessy ad David A. Patterson

Microprocessor Architecture, Jean-Loup Baer

Game Engine Gems 2, Eric Lengyel

Compilers, Alfred V. Aho, Monica S. Lam, Ravi Sethi and Jeffrey D. Ullman

Advanced Windows Debugging, Mario Hewardt and Daniel Pravat

The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani and Jerome Friedman

Data Structures and Algorithms Made Easy, Narasimha Karumanchi

Code, Charles Petzold

Pattern Recognition and Machine Learning, Christopher Bishop

Fluent Python, Luciano Ramalho

Learning Deep Architectures for AI, Yoshua Bengio

Deep Learning Methods and Applications, Li Deng and Dong Yu

Books that I will read soon

Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville

Natural Language Processing with Python, Steven Bird, Ewan Klein and Edward Loper

Advanced Machine Learning with Python, John Hearty

Books that I may read in the future

More Effective C++, Scott Meyers

Effective C++, Scott Meyers

The Go Programming Language, Alan A. A. Donovan and Brian W. Kernighan

Algorithms Unlocked, Thomas H. Cormen

The D Programming Language, Andrei Alexandrescu

Algorithms in C++, Parts 1-4, Robert Sedgewick

Algorithms in C++ Part 5: Graph Algorithms, Robert Sedgewick

Programming Challenges, Steven S Skiena and Miguel A. Revilla

C++ Templates: The Complete Guide, David Vandevoorde and Nicolai M. Josuttis

The C++ Programming Language, Bjarne Stroustrup

Computer Age Statistical Inference: Algorithms, Evidence, and Data Science, Bradley Efron and Trevor Hastie

Data Structure and Algorithmic Thinking with Python: Data Structure and Algorithmic Puzzles, Narasimha Karumanchi

Algorithm Design, Jon Kleinberg and Eva Tardos

The Art of Computer Programming, Volumes 1-4A, Donald E. Knuth

Concrete Mathematics: A Foundation for Computer Science, Ronald L. Graham, Donald E. Knuth and Oren Patashnik

Fundamentals of Machine Learning for Predictive Data Analytics, John D. Kelleher Brian Mac Namee and Aoife D'Arcy

CUDA Programming: A Developer's Guide to Parallel Computing with GPUs, Shane Cook

Artificial Intelligence: A Modern Approach, Stuart Russell

Distributed Systems: Concepts and Design, George Coulouris, Jean Dollimore, Tim Kindberg and Gordon Blair

Multicore and GPU Programming, Gerassimos Barlas