Deep Reinforcement Learning Hands-On: Apply modern RL methods with deep Q-networks value iteration policy gradients TRPO AlphaGo Zero and more (English Edition)


Deep Reinforcement Learning Hands-On: Apply modern RL methods with deep Q-networks value iteration policy gradients TRPO AlphaGo Zero and more (English Edition)

Bücher Details

  • Titel:Deep Reinforcement Learning Hands-On: Apply modern RL methods with deep Q-networks value iteration policy gradients TRPO AlphaGo Zero and more (English Edition)
  • Dateiname: deep-reinforcement-learning-hands-on-apply-modern-rl-methods-with-deep-q-networks-value-iteration-policy-gradients-trpo-alphago-zero-and-more-english-edition.pdf
  • ISBN: 1019221788834240
  • Datum des Hochladens: 2020-01-15
  • Anzahl der Seiten: 527 Seiten
  • Autor: Maxim Lapan
  • Verlag: Maxim Lapan

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Überblick:

Über den Autor und weitere Mitwirkende Maxim Lapan is a deep learning enthusiast and independent researcher. His background and 15 years' work expertise as a software developer and a systems architect lays from low-level Linux kernel driver development to performance optimization and design of distributed applications working on thousands of servers. With vast work experiences in big data Machine Learning and large parallel distributed HPC and nonHPC systems he has a talent to explain a gist of complicated things in simple words and vivid examples. His current areas of interest lie in practical applications of Deep Learning such as Deep Natural Language Processing and Deep Reinforcement Learning. Maxim lives in Moscow Russian Federation with his family and he works for an Israeli start-up as a Senior NLP developer.

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