Book Image

Artificial Intelligence By Example

By : Denis Rothman
Book Image

Artificial Intelligence By Example

By: Denis Rothman

Overview of this book

Artificial intelligence has the potential to replicate humans in every field. Artificial Intelligence By Example serves as a starting point for you to understand how AI is built, with the help of intriguing examples and case studies. Artificial Intelligence By Example will make you an adaptive thinker and help you apply concepts to real-life scenarios. Using some of the most interesting AI examples, right from a simple chess engine to a cognitive chatbot, 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 IoT, and develop emotional quotient in chatbots using neural networks. You will move on to designing AI solutions in a simple manner rather than get confused by complex architectures and techniques. This comprehensive guide will be a starter kit for you to develop AI applications on your own. By the end of this book, you will have understood the fundamentals of AI and worked through a number of case studies that will help you develop your business vision.
Table of Contents (19 chapters)

Checking whether AI can be avoided

Trying to avoid AI in a book on AI may seem paradoxical. However, AI tools can be compared to having a real toolkit in real life. If you are at home and you just need to change a light bulb, you do not have to get your brand new toolkit to show off to everybody around you. You just change the light bulb and that's it.

In AI, as in real life, use the right tools at the right time. If AI is not necessary to solve a problem, do not use it.

Use a proof of concept (POC) approach to prove your point for an AI project. A POC should cost much less than the project itself and helps to build a team that believes in the outcome. The first step is exploring the data volume and the method that will be used.

Data volume and applying k-means clustering

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