Hands-On Neural Networks

By : Leonardo De Marchi, Laura Mitchell

3.5 (2)

Buy this Book

Hands-On Neural Networks

3.5 (2)

By: Leonardo De Marchi, Laura Mitchell

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Section 1: Getting Started

Getting Started with Supervised Learning

History of AI

An overview of machine learning

Environment setup

Supervised learning in practice with Python

Feature engineering

Supervised learning algorithms

Summary

Neural Network Fundamentals

The perceptron

Keras

Feedforward neural networks

FFNN in Python from scratch

Summary

Section 2: Deep Learning Applications

Convolutional Neural Networks for Image Processing

Understanding CNNs

Convolutional layers

CNNs in Keras

Keras for expression recognition

Optimizing the network

Summary

Exploiting Text Embedding

Machine learning for NLP

Understanding word embeddings

GloVe

Summary

Working with RNNs

Understanding RNNs

Long Short-Term Memory

LSTMs in Keras

Summary

Reusing Neural Networks with Transfer Learning

Transfer learning theory

Implementing MTL

Feature extraction

Implementing TL in PyTorch

Summary

Section 3: Advanced Applications

Working with Generative Algorithms

Discriminative versus generative algorithms

Understanding GANs

GAN variations and timelines

BigGAN

StyleGAN

Deepfakes

RadialGAN

Summary

Environment setup

There are only a few viable programming language options when creating ML software. The most popular ones are Python and R, but Scala is also quite popular. There are other languages, but the better ones in terms of use in ML are Julia, JavaScript, Java, and a few others. In this book, we will be using Python only. The motivation behind this choice is its widespread adoption, its simplicity of use, and the vast ecosystem of libraries that are possible to use.

In particular, we will be using Python 3.7 and a few of its following libraries: