A Primer on Scientific Programming with Python

ISBN: 3642024742, Springer, 693 pages (July 2009)

An example- and problem-oriented introduction to computer programming of scientific applications.

NumPy 1.5 Beginner's Guide

ISBN: 1849515301, Packt Publishing, 234 pages (November 2011)

An action-packed guide for the easy-to-use, high performance, Python based free open source NumPy mathematical library using real-world examples.

Participatory Geospatial Development Using Python

ISBN: 1479316474, CreateSpace, 146 pages (September 2012)

The book contains discussion on raster and vector data processing using Python binding of GDAL/OGR library. Also, different approaches of representing spatial reference system are enumerated. There is a discussion on LIDAR data processing using Python binding of libLAS library. Apart from processing geospatial data, the book also covers plotting of geospatial data. The last chapter deals with freely available geospatial data, such as ASTER GDEM, SRTM data etc.

Building Machine Learning Systems with Python

ISBN: 1782161406, PACKT Publishing, 350 pages (September 2013)

Bioinformatics Programming Using Python

ISBN: 059615450X, O'Reilly Media, 528 pages (December 2009)

Python for Finance

ISBN: 1783284374, PACKT Publishing, 408 pages (April 2014)

Genetic Algorithms with Python, Leanpub (April 2016)

This book provides a hands-on introduction to machine learning with genetic algorithms using Python. It features working, step-by-step code examples, that give you experience solving problems with genetic algorithms. Topics covered include handling multiple fitness goals, phenotype vs genotype, gene constraints, memetic algorithms, local minimums and maximums, simulated annealing, branch and bound, variable length chromosomes, using one genetic algorithm to tune another, and genetic programming.

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