Book Image

Hands-On Neural Networks with Keras

By : Niloy Purkait
Book Image

Hands-On Neural Networks with Keras

By: Niloy Purkait

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)
Free Chapter
1
Section 1: Fundamentals of Neural Networks
5
Section 2: Advanced Neural Network Architectures
10
Section 3: Hybrid Model Architecture
13
Section 4: Road Ahead

Reinforcement Learning with Deep Q-Networks

In the last chapter, we saw how recursive loops, information gates, and memory cells can be used to model complex time-dependent signals with neural networks. More specifically, we saw how the Long Short-Term Memory (LSTM) architecture leverages these mechanics to preserve prediction errors and backpropagate them over increasingly long time steps. This allowed our system to inform predictions using both short-term (that is, from information relating to the immediate environment) and long-term representations (that is, from information pertaining to the environment that was observed long ago).

The beauty of the LSTM lies in the fact that it is able to learn and preserve useful representations over very large periods of time (up to a thousand time steps). By maintaining a constant error flow through the architecture, we can implement a...