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Deep Reinforcement Learning in Action (Paperback)
Loot Price: R1,333
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Deep Reinforcement Learning in Action (Paperback)
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Humans learn best from feedback-we are encouraged to take actions
that lead to positive results while deterred by decisions with
negative consequences. This reinforcement process can be applied to
computer programs allowing them to solve more complex problems that
classical programming cannot. Deep Reinforcement Learning in Action
teaches you the fundamental concepts and terminology of deep
reinforcement learning, along with the practical skills and
techniques you'll need to implement it into your own projects. Key
features * Structuring problems as Markov Decision Processes *
Popular algorithms such Deep Q-Networks, Policy Gradient method and
Evolutionary Algorithms and the intuitions that drive them *
Applying reinforcement learning algorithms to real-world problems
Audience You'll need intermediate Python skills and a basic
understanding of deep learning. About the technology Deep
reinforcement learning is a form of machine learning in which AI
agents learn optimal behavior from their own raw sensory input. The
system perceives the environment, interprets the results of its
past decisions, and uses this information to optimize its behavior
for maximum long-term return. Deep reinforcement learning famously
contributed to the success of AlphaGo but that's not all it can do!
Alexander Zai is a Machine Learning Engineer at Amazon AI working
on MXNet that powers a suite of AWS machine learning products.
Brandon Brown is a Machine Learning and Data Analysis blogger at
outlace.com committed to providing clear teaching on difficult
topics for newcomers.
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