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Hands-On Artificial Intelligence for Beginners
Hands-On Artificial Intelligence for Beginners

Hands-On Artificial Intelligence for Beginners: An introduction to AI concepts, algorithms, and their implementation

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Profile Icon Patrick D. Smith Profile Icon Dindi
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Paperback Oct 2018 362 pages 1st Edition
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Arrow left icon
Profile Icon Patrick D. Smith Profile Icon Dindi
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Paperback Oct 2018 362 pages 1st Edition
eBook
€8.99 €32.99
Paperback
€41.99
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Free Trial
Renews at €18.99p/m
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Hands-On Artificial Intelligence for Beginners

Machine Learning Basics

Artificial Intelligence (AI) is rooted in mathematics and statistics. When creating an Artificial Neural Network (ANN), we're conducting mathematical operations on data represented in linear space; it is, by nature, applied mathematics and statistics. Machine learning algorithms are nothing but function approximations; they try and find a mapping between an input and a correct corresponding output. We use algebraic methods to create algorithms that learn these mappings.

Almost all machine learning can be expressed in a fairly straight-forward formula; bringing together a dataset and model, along with a loss function and optimization technique that are applicable to the dataset and model. This section is intended as a review of the basic mathematical tools and techniques that are essential to understanding what's under the hood in AI.

In this chapter...

Technical requirements

In this chapter, we will be working in Python 3 with the scikit-learn scientific computing package. You can install the package, you can run pip install sklearn in your terminal or command line.

Applied math basics

When we talk about mathematics as related to deep learning and AI, we're often talking about linear algebra. Linear algebra is a branch of continuous mathematics that involves the study of vector space and operations performed in vector space. If you remember back to grade-school algebra, algebra in general deals with unknown variables. With linear algebra, we're extending this study into linear systems that have an arbitrary number of dimensions, which is what makes this a form of continuous mathematics.

AI relies on the basic building block of the tensor. Within AI, these mathematical objects store information throughout ANNs that allow them to operate; they are data structures that are utilized throughout AI. As we will see, a tensor has a rank, which essentially tells us about the indices of the data (how many rows and columns the data has).

...

Basic statistics and probability theory

Probability, the mathematical method for modeling uncertain scenarios, underpins the algorithms that make AI intelligent, helping to tell us how our systems should reason. So, what is probability? We'll define it as follows:

Probability is a frequency expressed as a fraction of the sample size, n [1].

Simply said, probability is the mathematical study of uncertainty. In this section, we'll cover the basics of probability space and probability distributions, as well as helpful tools for solving simple problems.

The probability space and general theory

When probability is discussed, it's often referred to in terms of the probability of a certain event happening. Is it going...

Constructing basic machine learning algorithms

As mentioned in the last chapter, machine learning is a term that was developed as a reaction to the first AI winter. Today, we generally consider machine learning to be the overarching subject area for deep learning and ANNs in general.

Most machine learning solutions can be broken down into either a classification problem or a regression problem. A classification problem is when the output variables are categorical, such as fraud or not fraud. A regression problem is when the output is continuous, such as dollars or site visits. Problems with numerical output can be categorical, but are typically transformed to have a categorical output such as first class and second class.

Within machine learning, we have supervised algorithms and unsupervised algorithms. In this section, we will introduce these types of algorithms and explore...

Basic tuning

So you've built a model, now what? Can you call it a day? Chances are, you'll have some optimization to do on your model. A key part of the machine learning process is the optimization of our algorithms and methods. In this section, we'll be covering the basic concepts of optimization, and will be continuing our learning of tuning methods throughout the following chapters.

Sometimes, when our models do not perform well with new data it can be related to them overfitting or underfitting. Let's cover some methods that we can use to prevent this from happening. First off, let's look at the random forest classifier that we trained earlier. In your notebook, call the predict method on it and pass the x_test data in to receive some predictions:

predicted = rf_classifier.predict(x_test)

From this, we can create evaluate the performance of our classifier...

Summary

Machine learning, and by extension, deep learning, relies on the building blocks of linear algebra and statistics at its core. Vectors, matrices, and tensors provide the means by which we represent input data and parameters in machine learning algorithms, and the computations between these are the core operations of these algorithms. Likewise, distributions and probabilities help us model data and events in machine learning.

We also covered two classes of algorithms that will inform how we think about ANNs in further chapters: supervised learning methods and unsupervised learning methods. With supervised learning, we provide the algorithm with a set of features and labels, and it learns how to appropriately map certain feature combinations to labels. In unsupervised learning, the algorithm isn't provided with any labels at all, and it must infer relationships and...

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Key benefits

  • Enter the world of AI with the help of solid concepts and real-world use cases
  • Explore AI components to build real-world automated intelligence
  • Become well versed with machine learning and deep learning concepts

Description

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.

Who is this book for?

This book is designed for beginners in AI, aspiring AI developers, as well as machine learning enthusiasts with an interest in leveraging various algorithms to build powerful AI applications.

What you will learn

  • Use TensorFlow packages to create AI systems
  • Build feedforward, convolutional, and recurrent neural networks
  • Implement generative models for text generation
  • Build reinforcement learning algorithms to play games
  • Assemble RNNs, CNNs, and decoders to create an intelligent assistant
  • Utilize RNNs to predict stock market behavior
  • Create and scale training pipelines and deployment architectures for AI systems

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Publication date : Oct 31, 2018
Length: 362 pages
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Language : English
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Length: 362 pages
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Language : English
ISBN-13 : 9781788991063
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Table of Contents

14 Chapters
The History of AI Chevron down icon Chevron up icon
Machine Learning Basics Chevron down icon Chevron up icon
Platforms and Other Essentials Chevron down icon Chevron up icon
Your First Artificial Neural Networks Chevron down icon Chevron up icon
Convolutional Neural Networks Chevron down icon Chevron up icon
Recurrent Neural Networks Chevron down icon Chevron up icon
Generative Models Chevron down icon Chevron up icon
Reinforcement Learning Chevron down icon Chevron up icon
Deep Learning for Intelligent Agents Chevron down icon Chevron up icon
Deep Learning for Game Playing Chevron down icon Chevron up icon
Deep Learning for Finance Chevron down icon Chevron up icon
Deep Learning for Robotics Chevron down icon Chevron up icon
Deploying and Maintaining AI Applications Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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