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
Section 1: Getting Started
Section 2: Deep Learning Applications
Section 3: Advanced Applications

Basic definitions

Recently, RL has been gaining more and more popularity. Notably, many of its breakthroughs have come from improvements from supervised methods such as deep learning.

At the moment, most RL algorithms are used in virtual environments such as video games. Luckily, there are companies, such as OpenAI, that have created and released learning environments where it's easy to test the algorithm in different environments.

It's possible to download this learning environment, called Gym, from OpenAI's website.

Additionally, there are real-world applications on RL, and some of them are incredibly impactful. For example, DeepMind, after being used to optimize Google's data centers, was able to reduce the energy consumption and overall energy bill of Google's data centers by 10% and 40%.

A major problem in these algorithms is generalizing the learning...