Book Image

The Applied Artificial Intelligence Workshop

By : Anthony So, William So, Zsolt Nagy
Book Image

The Applied Artificial Intelligence Workshop

By: Anthony So, William So, Zsolt Nagy

Overview of this book

You already know that artificial intelligence (AI) and machine learning (ML) are present in many of the tools you use in your daily routine. But do you want to be able to create your own AI and ML models and develop your skills in these domains to kickstart your AI career? The Applied Artificial Intelligence Workshop gets you started with applying AI with the help of practical exercises and useful examples, all put together cleverly to help you gain the skills to transform your career. The book begins by teaching you how to predict outcomes using regression. You will then learn how to classify data using techniques such as k-nearest neighbor (KNN) and support vector machine (SVM) classifiers. As you progress, you’ll explore various decision trees by learning how to build a reliable decision tree model that can help your company find cars that clients are likely to buy. The final chapters will introduce you to deep learning and neural networks. Through various activities, such as predicting stock prices and recognizing handwritten digits, you’ll learn how to train and implement convolutional neural networks (CNNs) and recurrent neural networks (RNNs). By the end of this applied AI book, you’ll have learned how to predict outcomes and train neural networks and be able to use various techniques to develop AI and ML models.

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Table of Contents (8 chapters)
Preface
About the Book
1
1. Introduction to Artificial Intelligence
1. Introduction to Artificial Intelligence
Introduction
Fields and Applications of AI
AI Tools and Learning Models
The Role of Python in AI
Python for Game AI
Heuristics
Pathfinding with the A* Algorithm
Introducing the A* Algorithm
Game AI with the Minmax Algorithm and Alpha-Beta Pruning
The Minmax Algorithm
DRYing Up the Minmax Algorithm – the NegaMax Algorithm
Summary
Free Chapter
2
2. An Introduction to Regression
2. An Introduction to Regression
Introduction
Linear Regression with One Variable
Linear Regression with Multiple Variables
Polynomial and Support Vector Regression
Support Vector Regression
Summary
3
3. An Introduction to Classification
3. An Introduction to Classification
Introduction
The Fundamentals of Classification
Data Preprocessing
The K-Nearest Neighbors Classifier
Classification with Support Vector Machines
Summary
4
4. An Introduction to Decision Trees
4. An Introduction to Decision Trees
Introduction
Decision Trees
The Confusion Matrix
Random Forest Classifier
Summary
5
5. Artificial Intelligence: Clustering
5. Artificial Intelligence: Clustering
Introduction
Defining the Clustering Problem
Clustering Approaches
The K-Means Algorithm
The Mean Shift Algorithm
Clustering Performance Evaluation
Summary
6
6. Neural Networks and Deep Learning
6. Neural Networks and Deep Learning
Introduction
Artificial Neurons
Neurons in TensorFlow
Neural Network Architecture
Activation Functions
Forward Propagation and the Loss Function
Backpropagation
Optimizers and the Learning Rate
Regularization
Deep Learning
Computer Vision and Image Classification
Recurrent Neural Networks (RNNs)
Summary
Appendix
1. Introduction to Artificial Intelligence
2. An Introduction to Regression
3. An Introduction to Classification
4. An Introduction to Decision Trees
5. Artificial Intelligence: Clustering
6. Neural Networks and Deep Learning

Recurrent Neural Networks (RNNs)

In the last section, we learned how we can use CNNs for computer vision tasks such as classifying images. With deep learning, computers are now capable of achieving and sometimes surpassing human performance. Another field that is attracting a lot of interest from researchers is natural language processing. This is a field where RNNs excel.

In the last few years, we have seen a lot of different applications of RNN technology, such as speech recognition, chatbots, and text translation applications. But RNNs are also quite performant in predicting time series patterns, something that's used for forecasting stock markets.

RNN Layers

The common point with all the applications mentioned earlier is that the inputs are sequential. There is a time component with the input. For instance, a sentence is a sequence of words, and the order of words matters; stock market data consists of a sequence of dates with corresponding stock prices.

To accommodate...

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