Book Image

Hands-On Neural Networks

By : Leonardo De Marchi, Laura Mitchell
Book Image

Hands-On Neural Networks

By: Leonardo De Marchi, Laura Mitchell

Overview of this book

Neural networks play a very important role in deep learning and artificial intelligence (AI), with applications in a wide variety of domains, right from medical diagnosis, to financial forecasting, and even machine diagnostics. Hands-On Neural Networks is designed to guide you through learning about neural networks in a practical way. The book will get you started by giving you a brief introduction to perceptron networks. You will then gain insights into machine learning and also understand what the future of AI could look like. Next, you will study how embeddings can be used to process textual data and the role of long short-term memory networks (LSTMs) in helping you solve common natural language processing (NLP) problems. The later chapters will demonstrate how you can implement advanced concepts including transfer learning, generative adversarial networks (GANs), autoencoders, and reinforcement learning. Finally, you can look forward to further content on the latest advancements in the field of neural networks. By the end of this book, you will have the skills you need to build, train, and optimize your own neural network model that can be used to provide predictable solutions.
Table of Contents (16 chapters)
Free Chapter
1
Section 1: Getting Started
4
Section 2: Deep Learning Applications
9
Section 3: Advanced Applications

Introducing Q-learning

There are many different types of RL algorithms; the main distinction is between the model-based and model-free RL algorithms. What we model about the environment is shown in the following diagram:

Some simple examples of RL algorithms

Model-based RL, as the name suggests, already starts with an idea of the world. This allows the agent to plan and think ahead. One of the problems with this approach is that, usually, the true model of the environment is not available and the model has to learn it by experience. An example of this is AlphaZero, from DeepMind, which was trained by self-play.

On the other hand, we have model-free methods, which, of course, don't use a model. One of the main advantages of this method is the sample efficiency and the fact that (currently) these models are easier to work with and improve.

In this chapter, we will focus on...