You may recall that a random approach to solving the taxi cab simulation took our agent about 6,000 time steps. Sometimes, out of sheer luck, you may be able to solve it under 2,000 time steps. However, we can further tip the odds in our favor by implementing a version of the Bellman equation. This approach will essentially allow our agent to remember its actions and corresponding rewards per state by using a Q-table. We can implement this Q-table on Python using a NumPy array, with dimensions corresponding to our observation space (the number of different possible states) and action space (the number of different possible actions our agent can make) in the taxi cab environment. Recall that the taxi cab simulation has an environment space of 500 and an action space of six, making our Q-table a matrix of 500 rows and six columns. We can...
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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