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

Getting Started with Supervised Learning

Artificial Intelligence (AI) is now a buzzword that is added to products and services to make them more appealing, and more often than not, it's a marketing strategy rather than a technical achievement. Most of the time, AI is used as an umbrella term to describe anything from simple analytics to advanced learning algorithms. It's something that sells, as most of the population does not have much knowledge about it, but intuitively everyone now understands that it is something that will change the world we live in.

Luckily, it's not just hype, and we have seen many astonishing achievements made by AI, such as Tesla's self-driving cars. Using recent research into deep neural networks, Tesla managed to create a functionality and made it available to the masses much quicker than most of the experts predicted.

In this book, we will try to steer away from the hype and focus on the actual value that AI can provide, starting from the basics in order to rapidly ramp up to the most recent algorithms.

This chapter will cover the following topics in detail:

  • History of AI
  • An overview of machine learning
  • Environment setup
  • Supervised learning in practice with Python
  • Feature engineering
  • Supervised learning algorithms