Deep Reinforcement Learning (Softcover)

Name: Deep Reinforcement Learning
Author: Mohit Sewak

Frontiers of Artificial Intelligence

von Mohit Sewak Verlag: Springer Singapore

1st ed. 2019 - Erschienen 15.08.2020 - Kartoniert, 224 Seiten, 235mm x 155mm x 12mm, Sprache(n): eng

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Presents comprehensive insights into advanced deep learning concepts like the 'hard attention mechanism'Introduces algorithms that are slated to become the future of artificial intelligenceAllows readers to gain an understanding of algorithms such as TD Learning and Deep Q Learning, and Asynchronous Advantage Actor-Critic Models

This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book not only equips readers with an understanding of multiple advanced and innovative algorithms, but also prepares them to implement systems such as those created by Google Deep Mind in actual code. This book is intended for readers who want to both understand and apply advanced concepts in a field that combines the best of two worlds ¿ deep learning and reinforcement learning ¿ to tap the potential of ¿advanced artificial intelligence¿ for creating real-world applications and game-winning algorithms.

Mr. Sewak has been the Lead Data Scientist/Analytics Architect for a number of important international AI/DL/ML software and industry solutions and has also been involved in providing solutions and research for a series of cognitive features for IBM Watson Commerce. He has 14 years of experience working as a solutions architect using technologies like TensorFlow, Torch, Caffe, Theano, Keras, Open AI, SpaCy, Gensim, NLTK, Watson, SPSS, Spark, H2O, Kafka, ES, and others.

Mr. Sewak has been the Lead Data Scientist/Analytics Architect for a number of important international AI/DL/ML software and industry solutions and has also been involved in providing solutions and research for a series of cognitive features for IBM Watson Commerce. He has 14 years of experience working as a solutions architect using technologies like TensorFlow, Torch, Caffe, Theano, Keras, Open AI, SpaCy, Gensim, NLTK, Watson, SPSS, Spark, H2O, Kafka, ES, and others.

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