As we can see, the efficiency of our agent to solve an environment depends on what policy it uses to match state-action pairs at each time step. Hence, a function, known as a policy function (π), can specify the combination of state-action pairs for each time step the agent comes across. As the simulation runs, the policy is responsible for producing the trajectory, which is composed of game-states; actions that are taken by our agent as a response; and a reward that's generated by the environment, as well as the next state of the game the agent receives. Intuitively, you can think of a policy as a heuristic that generates actions that respond to the generated states of an environment. A policy function itself can be a good or a bad one. If your policy is to shoot first and ask questions later, you may end up shooting a hostage. Hence...
Hands-On Neural Networks with Keras
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Hands-On Neural Networks with Keras
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Overview of this book
Neural networks are used to solve a wide range of problems in different areas of AI and deep learning.
Hands-On Neural Networks with Keras will start with teaching you about the core concepts of neural networks. You will delve into combining different neural network models and work with real-world use cases, including computer vision, natural language understanding, synthetic data generation, and many more. Moving on, you will become well versed with convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, autoencoders, and generative adversarial networks (GANs) using real-world training datasets. We will examine how to use CNNs for image recognition, how to use reinforcement learning agents, and many more. We will dive into the specific architectures of various networks and then implement each of them in a hands-on manner using industry-grade frameworks.
By the end of this book, you will be highly familiar with all prominent deep learning models and frameworks, and the options you have when applying deep learning to real-world scenarios and embedding artificial intelligence as the core fabric of your organization.
Table of Contents (16 chapters)
Preface
Overview of Neural Networks
A Deeper Dive into Neural Networks
Signal Processing - Data Analysis with Neural Networks
Section 2: Advanced Neural Network Architectures
Convolutional Neural Networks
Recurrent Neural Networks
Long Short-Term Memory Networks
Reinforcement Learning with Deep Q-Networks
Section 3: Hybrid Model Architecture
Autoencoders
Generative Networks
Section 4: Road Ahead
Contemplating Present and Future Developments
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