- Deep learning and machine learning mean the same thing. (Yes | No)
- Deep learning networks mostly reproduce human brain functions (Yes | No)
- Overfitting is unacceptable. (Yes | No)
- Transfer learning can save the cost of building another model. (Yes | No)
- Training a corporate model on MNIST is enough to implement it on a production line, for example. (Yes | No)
- Exploring artificial intelligence beyond the cutting edge is not necessary. It is easier to wait for the next ideas that are published. (Yes | No)
- Some researchers have reproduced all the physical and biological reasoning functions of the human brain in robots. In fact, some robots have human brain clones in them. (Yes | No)
- Artificial general intelligence software, a program that can adapt to any human function (natural language processing, image processing, or sound streams) better than a human, already exists...
Artificial Intelligence By Example
By :
Artificial Intelligence By Example
By:
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)
Preface
Free Chapter
Become an Adaptive Thinker
Think like a Machine
Apply Machine Thinking to a Human Problem
Become an Unconventional Innovator
Manage the Power of Machine Learning and Deep Learning
Don't Get Lost in Techniques – Focus on Optimizing Your Solutions
When and How to Use Artificial Intelligence
Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies
Getting Your Neurons to Work
Applying Biomimicking to Artificial Intelligence
Conceptual Representation Learning
Automated Planning and Scheduling
AI and the Internet of Things (IoT)
Optimizing Blockchains with AI
Cognitive NLP Chatbots
Improve the Emotional Intelligence Deficiencies of Chatbots
Quantum Computers That Think
Answers to the Questions
Customer Reviews