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Artificial Intelligence By Example

Artificial Intelligence By Example - Second Edition

By : Denis Rothman
4.6 (17)
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Artificial Intelligence By Example

Artificial Intelligence By Example

4.6 (17)
By: Denis Rothman

Overview of this book

AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing. By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions.
Table of Contents (23 chapters)
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21
Other Books You May Enjoy
22
Index

Innovating AI with Google Translate

In this chapter, we will illustrate how to innovate existing AI through Google Translate. First, we will start by understanding the difference between inventions, innovations, disruption, high-priced products, and revolutions, and how we can create an impact on an AI innovation.

Sometimes, a developer will confuse an invention with innovation, leading to a major failure in a key AI project. Some AI designers take a revolution to mean a disruption, leading to early sales and then nothing.

Once we have defined the key concepts of innovation, we will use Google Translate to understand the linguistic principles that surround natural language processing (NLP).

Google Translate entered the market in 2006 and was enhanced by neural networks in 2016, but still, it often produces bad answers. Is that good news or bad news? We will implement Google's API in a Python program to find out how to uncover false translations from...

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Artificial Intelligence By Example
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