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

Datasets and libraries

Now that we have covered DBNs from a theoretical perspective, we take a look at some examples of code using the TensorFlow library along with the TensorFlow DBN Git repository (https://github.com/albertbup/deep-belief-network/). The repository allows you to develop simple, fast, Python implementations of DBN, which is based on binary RBMs.

We will consider the following two commonly used datasets in the machine learning community, in order to do so:

MNIST dataset: For this dataset, you can refer to Chapter 3, Convolutional Neural Networks for Image Processing. This is a dataset of images, each of which displays a number from 0-9. Each image is 28 pixels in height and 28 pixels in width. It is available in the sklearn library but can be downloaded from the following web page, http://yann.lecun.com/exdb/mnist/: