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


While the majority of well-known GANs are used for image creations, RadialGAN is designed for numerical analysis. If we consider an example where we want to evaluate how effective a new medical treatment is, we would need to combine data from a number of different hospitals in order to ensure we have enough data to make concrete conclusions. However, this poses problems such as different hospitals measuring outcomes in different ways, using laboratories that give different results in different environments, and so on. In order to address this problem, RadialGAN firstly transforms the dataset from each hospital into latent space, which allows us to hold the data from different sources in a uniform format. From here, the latent space data can be converted into the feature space of each unique dataset.

Each dataset considered by the RadialGAN has an encoder neural network...