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

Neural Network Fundamentals

Artificial neural networks (ANNs) are a set of bio-inspired algorithms. In particular, they are loosely inspired by biological brains; exactly like animal brains, ANNs consist of simple units (neurons) connected to each other. In biology, these units are called neurons. They receive, process, and transmit a signal to other neurons, acting like a switch.

The elements of a neural network are quite simple on their own; the complexity and the power of these systems come from the interaction between the elements. A human brain has more than 100 billion neurons and 100 trillion connections.

In the previous chapter, we introduced a supervised learning problem. In this chapter, we will cover the main building blocks to create Neural Networks (NNs) to solve such a problem. We will cover all of the elements to create a feedforward neural network, and we&apos...