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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Section 1: Getting Started

Section 1: Getting Started

Getting Started with Supervised Learning

Getting Started with Supervised Learning

An overview of machine learning

Environment setup

Supervised learning in practice with Python

Feature engineering

Supervised learning algorithms

Neural Network Fundamentals

Neural Network Fundamentals

Feedforward neural networks

FFNN in Python from scratch

Section 2: Deep Learning Applications

Section 2: Deep Learning Applications

Convolutional Neural Networks for Image Processing

Convolutional Neural Networks for Image Processing

Understanding CNNs

Convolutional layers

Keras for expression recognition

Optimizing the network

Exploiting Text Embedding

Exploiting Text Embedding

Machine learning for NLP

Understanding word embeddings

Working with RNNs

Working with RNNs

Understanding RNNs

Long Short-Term Memory

Reusing Neural Networks with Transfer Learning

Reusing Neural Networks with Transfer Learning

Transfer learning theory

Implementing MTL

Feature extraction

Implementing TL in PyTorch

Section 3: Advanced Applications

Section 3: Advanced Applications

Working with Generative Algorithms

Working with Generative Algorithms

Discriminative versus generative algorithms

Understanding GANs

GAN variations and timelines

Further reading

Implementing Autoencoders

Implementing Autoencoders

Overview of autoencoders

Autoencoder applications

Bottleneck and loss functions

Standard types of autoencoder

Variational Autoencoders

Further reading

Deep Belief Networks

Deep Belief Networks

Overview of DBNs

DBN architecture

Datasets and libraries

Further reading

Reinforcement Learning

Reinforcement Learning

Basic definitions

Introducing Q-learning

Playing with OpenAI Gym

The frozen lake problem

Whats Next?

Summarizing the book

Future of machine learning

Artificial general intelligence

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Feedforward neural networks

One of the main drawbacks of the perceptron algorithm is that it's only able to capture linear relationships. An example of a simple task that it's not able to solve is the logic XOR. The logic XOR is a very simple function in which the output is true only when its two pieces of binary input are different from each other. It can be described with the following table:

	`X2 = 0`	`X2 = 1`
`X1 = 0`	`False`	`True`
`X1 = 1`	`True`	`False`

The preceding table can be also represented with the following plot:

The XOR problem visualized

In the XOR problem, it's not possible to find a line that correctly divides the prediction space in two.

It's not possible to separate this problem using a linear function, so our previous perceptron would not help here. Now, the decision boundary in the previous example was a single line, so it's easy to...