The Reinforcement Learning Workshop : Learn How to Apply Cutting-Edge Reinforcement Learning Algorithms to a Wide Range of Control Problems. - Palmas, Alessandro; Ghelfi, Emanuele; Petre, Alexandra Galina; Kulkarni, Mayur; N.S., Anand; Nguyen, Quan.; Sen, Aritra; So, Anthony (Data scientist); Basak, Saikat (Birmingham : Packt Publishing, Limited, 2020)
With the help of practical examples and engaging activities, The Reinforcement Learning Workshop takes you through reinforcement learning's core techniques and frameworks. Following a hands-on approach, it allows you to learn reinforcement learning at your own pace to develop your own intelligent applications with ease.
Activity 2.01: Solving Gridworld -- Summary -- Chapter 3: Deep Learning in Practice with TensorFlow 2 -- Introduction -- An Introduction to TensorFlow and Keras -- TensorFlow -- Keras -- Exercise 3.01: Building a Sequential Model with the Keras High-Level API -- How to Implement a Neural Network Using TensorFlow -- Model Creation -- Model Training -- Loss Function Definition -- Optimizer Choice -- Learning Rate Scheduling -- Feature Normalization -- Model Validation -- Performance Metrics -- Model Improvement -- Overfitting -- Regularization -- Early Stopping -- Dropout -- Data Augmentation
Description based upon print version of record.
Batch Normalization.
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Keywords: Reinforcement learning; Algorithms; Algorithms; Apprentissage par renforcement (Intelligence artificielle); Algorithmes; algorithms; Programming & scripting languages: general; Artificial intelligence; Neural networks & fuzzy systems; Computers -- Intelligence (AI) & Semantics; Computers -- Neural Networks; Computers -- Programming Languages -- Python; Algorithms; Reinforcement learning