Looking for FREE Data Science books? Then you're in the right place.
Every month we feature 3 of the best free books on a variety of Data Science topics, from programming in R and Python to Statistics, Machine Learning and lots more.
These free books might be time limited (or not), and you'll find them in a variety of formats, from both top-tier publishers and self-published authors.
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This month we highlight these 3 books:
They're all FREE, so help yourselves...
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You can find further details in our TCs
By Valluru Rao
The extensively revised and updated edition provides a logical and easy-to-follow progression through C++ programming for two of the most popular technologies for artificial intelligence - neural and fuzzy programming. The authors cover theory as well as practical examples, giving programmers a solid foundation as well as working examples with reusable code.
The hybridization of the technologies is demonstrated on architectures such as Fuzzy-Back-propagation Networks (NN-FL), Simplified Fuzzy ARTMAP (NN-FL), and Fuzzy Logic.
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By Antoni Niederlinski
The book is an introductory and down-to-earth presentation of Constraint Logic Programming (CLP), an exciting software paradigm, more and more popular for solving combinatorial as well as continuous constraint satisfaction problems and constraint optimization problems.
It is based on the popular, intensively supported and documented ECLiPSe platform, freely available under Cisco-style Mozilla Public License.
The Author aims at teaching modeling i.e. translating verbal problem statements into Prolog or CLP programs. This has been dealt with by a series of problems of increasing complexity, translated into Prolog or CLP programs and running under ECLiPSe. The theoretical background has been minimized while stressing intuitive understanding.
Presented constraint satisfaction problems deal with finding feasible/optimal states, and feasible/optimal state-space trajectories, starting with simple puzzles and proceeding to advanced ones, like graph coloring problems, scheduling problems with particular attention to job-shop problems (including the famous MT10 benchmark), and Traveling Salesman Problems. The last chapter is concerned with Continuous Constraints Satisfaction and Constraint Optimization Problems.
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By Mark Watson
This book for both professional programmers and home hobbyists who already know how to program in Java and who want to learn practical Artificial Intelligence (AI) programming and information processing techniques.
In the style of a “cook book,” the chapters can be studied in any order. Each chapter follows the same pattern: a motivation for learning a technique, some theory for the technique, and a Java example program that you can experiment with.
This book uses both best of breed open source software and the author's own libraries to introduce the reader to Artificial Intelligence (AI) technologies like genetic algorithms, neural networks, expert systems, machine learning, and statistical natural language processing (NLP).
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