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)

Chapter 6 – Don't Get Lost in Techniques, Focus on Optimizing Your Solutions

1. Can a prototype be built with random data in corporate environments? (Yes | No)

The answer is yes and no. To start developing a prototype, using random data can help make sure that the basic algorithm works as planned.

However, once the prototype is advanced, it will be more reliable to use a well-designed dataset. Then, once the training has been successfully accomplished, random data can help again to see how your system behaves in all situations.

2. Do design matrices contain one example per matrix? (Yes | No)

The answer is no. A good design matrix contains one example in each row or each column depending on the shape you want it to have. But be careful; a design matrix that contains data that is too efficient might overfit. That means the learning algorithm will be efficient with that...