Discover how to train Deep Learning models by learning how to build real Deep Learning software libraries and verification software! The study of Deep Learning and Artificial Neural Networks (ANN) is a significant subfield of artificial intelligence (AI) that can be found within numerous fields: medicine, law, financial services, and science, for example. Just as the robot revolution threatened blue-collar jobs in the 1970s, so now the AI revolution promises a new era of productivity for white collar jobs. Important tasks have begun being taken over by ANNs, from disease detection and prevention, to reading and supporting legal contracts, to understanding experimental data, model protein folding, and hurricane modeling. AI is everywhere–on the news, in think tanks, and occupies government policy makers all over the world –and ANNs often provide the backbone for AI. Relying on an informal and succinct approach,Demystifying Deep Learning is a useful tool to learn the necessary steps to implement ANN algorithms by using both a software library applying neural network training and verification software. The volume offers explanations of how real ANNs work, and includes 6 practical examples that demonstrate in real code how to build ANNs and the datasets they need in their implementation, available in open-source to ensure practical usage. This approachable book follows ANN techniques that are used every day as they adapt to Natural Language Processing (NLP), image recognition, problem solving, and generative applications. This volume is an important introduction to the field, equipping the reader for more advanced study. Demystifying Deep Learning is ideal for engineers and professionals that need to learn and understand ANNs in their work.

Demystifying Deep Learning PDF

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