Book Image

Hands-On Artificial Intelligence for Beginners

By : Patrick D. Smith, David Dindi
Book Image

Hands-On Artificial Intelligence for Beginners

By: Patrick D. Smith, David Dindi

Overview of this book

Virtual Assistants, such as Alexa and Siri, process our requests, Google's cars have started to read addresses, and Amazon's prices and Netflix's recommended videos are decided by AI. Artificial Intelligence is one of the most exciting technologies and is becoming increasingly significant in the modern world. Hands-On Artificial Intelligence for Beginners will teach you what Artificial Intelligence is and how to design and build intelligent applications. This book will teach you to harness packages such as TensorFlow in order to create powerful AI systems. You will begin with reviewing the recent changes in AI and learning how artificial neural networks (ANNs) have enabled more intelligent AI. You'll explore feedforward, recurrent, convolutional, and generative neural networks (FFNNs, RNNs, CNNs, and GNNs), as well as reinforcement learning methods. In the concluding chapters, you'll learn how to implement these methods for a variety of tasks, such as generating text for chatbots, and playing board and video games. By the end of this book, you will be able to understand exactly what you need to consider when optimizing ANNs and how to deploy and maintain AI applications.

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Table of Contents (15 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
The History of AI
The History of AI
The beginnings of AI –1950–1974
Rebirth –1980–1987
The modern era takes hold – 1997-2005
Deep learning and the future – 2012-Present
Summary
2
Machine Learning Basics
Machine Learning Basics
Technical requirements
Applied math basics
Basic statistics and probability theory
Constructing basic machine learning algorithms
Basic tuning
Summary
3
Platforms and Other Essentials
Platforms and Other Essentials
Technical requirements
TensorFlow, PyTorch, and Keras
Cloud computing essentials
CPUs, GPUs, and other compute frameworks
Summary
4
Your First Artificial Neural Networks
Your First Artificial Neural Networks
Technical requirements
Network building blocks
The training process
Summary
5
Convolutional Neural Networks
Convolutional Neural Networks
Overview of CNNs
Convolutional layers
Pooling layers
Fully connected layers
The training process
CNNs for image tagging
Summary
6
Recurrent Neural Networks
Recurrent Neural Networks
Technical requirements
The building blocks of RNNs
Memory units – LSTMs and GRUs
Sequence processing with RNNs
Extensions of RNNs
Summary
7
Generative Models
Generative Models
Technical requirements
Getting to AI – generative models
Variational autoencoders
Generative adversarial networks
Other forms of generative models
Summary
References
8
Reinforcement Learning
Reinforcement Learning
Technical requirements
Principles of reinforcement learning
Q–learning
Policy optimization
Summary
9
Deep Learning for Intelligent Agents
Deep Learning for Intelligent Agents
Technical requirements
Word embeddings
Word2vec
GloVe
Constructing a basic agent
Summary
10
Deep Learning for Game Playing
Deep Learning for Game Playing
Technical requirements
Introduction
Networks for board games
Networks for video games
Summary
11
Deep Learning for Finance
Deep Learning for Finance
Requirements
Introduction to AI in finance
Deep learning in trading
Deep learning in asset management
Summary
12
Deep Learning for Robotics
Deep Learning for Robotics
Technical requirements
Introduction
Setting up your environment
Setting up a deep deterministic policy gradients model
Summary
References
13
Deploying and Maintaining AI Applications
Deploying and Maintaining AI Applications
Technical requirements
Introduction
Deploying your applications
Scaling your applications
Testing and maintaining your applications
Summary
References
14
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Memory units – LSTMs and GRUs

While regular RNNs can theoretically ingest information from long sequences, such as full documents, they are limited in how far back they can look to learn information. To overcome this, researchers have developed variants on the traditional RNN that utilize a unit called a memory cell, which helps the network remember important information. They were developed as a means to solve the vanishing gradient problem that occurs with traditional RNN models. There are two main variations of RNN that utilize memory cell architectures, known as the GRU and the LSTM. These architectures are the most widely used RNN architectures, so we'll pay some what attention to their mechanics.

LSTM

LSTMs...

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