So, let's try to understand the core mechanics of generative networks and how such approaches differ from the ones we already know. In our quest thus far, most of the networks we have implemented are for the purpose of executing a deterministic transformation of some inputs, in order to get to some sort of outputs. It was not until we explored the topic of reinforcement learning (Chapter 7, Reinforcement Learning with Deep Q-Networks) that we learned the benefits of introducing a degree of stochasticity (that is, randomness) to our modeling efforts. This is a core notion that we will be further exploring as we familiarize ourselves with the manner in which generative networks function. As we mentioned earlier, the central idea behind generative networks is to use a deep neural network to learn the probability distribution of variables...
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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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