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Hands-On Q-Learning with Python: Practical Q-learning with OpenAI Gym, Keras, and TensorFlow - Softcover
Synopsis
Leverage the power of reward-based training for your deep learning models with Python
Key Features
- Use Q-learning to train deep learning models using Markov decision processes (MDPs)
- Study practical deep reinforcement learning using deep Q-networks
- Explore state-based unsupervised learning for machine learning models
About the Author
Nazia Habib is a data scientist who has worked in a variety of industries to generate predictive analytics solutions for diverse groups of stakeholders. She is an expert in building solutions to optimization problems under conditions of uncertainty. Her projects range from predicting user behavior and engagement with social media apps to designing adaptive testing software. Her ongoing specialization is in designing custom reinforcement learning algorithms for...
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- PublisherPackt Publishing
- Publication date2019
- ISBN 10 1789345804
- ISBN 13 9781789345803
- BindingPaperback
- LanguageEnglish
- Number of pages212
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HandsOn QLearning with Python Practical Qlearning with OpenAI Gym, Keras, and TensorFlow
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Hands-On Q-Learning with Python
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ISBN 10: 1789345804
ISBN 13: 9781789345803
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Hands-On Q-Learning with Python: Practical Q-learning with OpenAI Gym, Keras, and TensorFlow
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Hands-On Q-Learning with Python: Practical Q-learning with OpenAI Gym, Keras, and TensorFlow
Published by
Packt Publishing Limited, 2019
ISBN 10: 1789345804
ISBN 13: 9781789345803
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Hands-On Q-Learning with Python
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Condition: New. Q-learning is the reinforcement learning approach behind Deep-Q-Learning and is a values-based learning algorithm in RL. This book will help you get comfortable with developing the effective agents for Q learning and also make you learn to effectively devel. Seller Inventory # 285682974
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