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Reinforcement and Systemic Machine Learning for Decision Making

Reinforcement and Systemic Machine Learning for Decision Making explores a newer and growing avenue of machine learning algorithm in the;

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Statistical Reinforcement Learning

is ideal for decision making in unknown environments with large amounts of data. Supplying an up-to-date and accessible introduction to the;

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Statistical Reinforcement Learning

is ideal for decision making in unknown environments with large amounts of data. Supplying an up-to-date and accessible introduction to the;

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Reinforcement Learning

Is For Data scientists, machine learning and deep learning professionals, developers who want to adapt and learn reinforcement learning.;

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Reinforcement Learning for Finance

library. Reinforcement Learning for Finance begins by describing methods for training neural networks. Next, it discusses CNN and RNN - two;

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Zai:Deep Reinforcement Learning i_p

of its past decisions, and uses this information to optimize its behavior for maximum long-term return. Deep reinforcement learning famously;

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Machine Learning for Intelligent Decision Science

The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for;

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Machine Learning for Intelligent Decision Science

The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for;

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Deep Reinforcement Learning with Python

Deep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics;

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Machine Learning based Natural Scene Recognition for Mobile Robot Localization i

feature representations and decision-making using reinforcement learning, laying the groundwork for future advances in mobile robot navigation;

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Machine Learning

Machine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the;

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Artificial Intelligence, Machine Learning, and Deep Learning

This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will;

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Applied Intelligent Decision Making in Machine Learning

, classification, clustering, pattern matching, feature selection, and deep learning in the decision-making process for several diversified applications;

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Grokking Deep Reinforcement Learning

and techniques for artificial general intelligence Deep reinforcement learning is a form of machine learning in which AI agents;

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The The Reinforcement Learning Workshop

Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and;

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Machine Learning for Finance

emphasis on financial applications Deep coverage of advanced machine learning approaches including neural networks, GANs, and reinforcement;

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Introduction to Machine Learning

, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing;

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Applied Reinforcement Learning with Python

Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important;

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Reinforcement Learning and Approximate Dynamic Programming for Feedback Control

Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and;

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Lecture Notes in Computational Intelligence and Decision Making

presenting the latest research in the fields of data mining, machine learning and decision-making. Divided into three sections: Analysis and Modeling;

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Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent

decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will;

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Human and Machine Learning

trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The;

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Applied Intelligent Decision Making in Machine Learning

handle the challenges related to decision making in various aspects. It incorporates the advances of machine intelligent techniques in decision;

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Foundations of Deep Reinforcement Learning

) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past;

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Towards a Theory for Designing Machine Learning Systems for Complex Decision Making Problems

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Recent Advances in Robot Learning

programming to reinforcement learning, is being applied to many subproblems in robot perception and control, often with objectives as diverse as;

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Hands-On Reinforcement Learning with Python

advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and;

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