Application of FPGA to Real-Time Machine Learning

This book lies at the interface of machine learning - a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail - and photonics - the physical science of light, which underlies many of the optical communications technologies used in our information society. It provides a thorough introduction to reservoir computing and field-programmable gate arrays (FPGAs).Recently, photonic implementations of reservoir computing (a machine learning algorithm based on artificial neural networks) have made a breakthrough in optical computing possible.

Application of FPGA to Real-Time Machine Learning features in the following genres: Laser physics, Computer vision, Artificial intelligence

Hardback. Not Available.

Application of FPGA to Real-Time Machine Learning was written by Piotr Antonik and published by Springer an imprint of Springer International Publishing

Application of FPGA to Real-Time Machine Learning has 171 pages

Yes it is part of Springer Theses series