Hands-On Neural Networks: Learn how to build and train your first neural network model using Python

Book Description

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.

What you will learn

• Learn how to train a network by using backpropagation
• Discover how to load and transform images for use in neural networks
• Study how neural networks can be applied to a varied set of applications
• Solve common challenges faced in neural network development
• Understand the transfer learning concept to solve tasks using Keras and Visual Geometry Group (VGG) network
• Get up to speed with advanced and complex deep learning concepts like LSTMs and NLP
• Explore innovative algorithms like GANs and deep reinforcement learning

Who this book is for

If you are interested in artificial intelligence and deep learning and want to further your skills, then this intermediate-level book is for you. Some knowledge of statistics will help you get the most out of this book.

Table of Contents

1. Getting started with Supervised Learning
2. Neural Network fundamentals
3. Convolutional Neural Networks  for image processing
4. Exploiting text embedding
5. Working with RNN
6. Reusing Neural Networks with Transfer Learning
7. Working with Generative Algorithms
8. Implementing Autoencoders
9. Working with Deep Belief Networks
10. Monte Carlo and Reinforcement Learning
11. What’s Next?