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

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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...

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.

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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