Last Algorithm

Despite five decades of research, parallel computing remains an exotic, frontier technology on the fringes of mainstream computing. Its much-heralded triumph over sequential computing has yet to materialize. This is in spite of the fact that the processing needs of many signal processing applications continue to eclipse the capabilities of sequential computing. The culprit is largely the software development environment. Fundamental shortcomings in the development environment of many parallel computer architectures thwart the adoption of parallel computing. Foremost, parallel computing has no unifying model to accurately predict the execution time of algorithms on parallel architectures. Cost and scarce programming resources prohibit deploying multiple algorithms and partitioning strategies in an attempt to find the fastest solution. As a consequence, algorithm design is largely an intuitive art form dominated by practitioners who specialize in a particular computer architecture. This, coupled with the fact that parallel computer architectures rarely last more than a couple of years, makes for a complex and challenging design environment.

Develop your Problem-Solving skill! Practical step-by-step guide for resolving Complex Problems.

Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Scikit-Learn

Enhance you object-oriented skills with data structure & algorithms

Data structure & algorithm deep dive

Learn all the Algorithm concepts in depth and in detail

This book presents state-of-the-art technical contributions based around one of the most successful evolutionary optimization algorithms published to date: Harmony Search. Contributions span from novel technical derivations