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.

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Table of Contents (19 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Become an Adaptive Thinker
Become an Adaptive Thinker
Technical requirements
How to be an adaptive thinker
The lessons of reinforcement learning
Summary
Questions
Further reading
2
Think like a Machine
Think like a Machine
Technical requirements
Designing datasets – where the dream stops and the hard work begins
Logistic activation functions and classifiers
Summary
Questions
Further reading
3
Apply Machine Thinking to a Human Problem
Apply Machine Thinking to a Human Problem
Technical requirements
Determining what and how to measure
Applying machine thinking to a human problem
Summary
Questions
Further reading
4
Become an Unconventional Innovator
Become an Unconventional Innovator
Technical requirements
The XOR limit of the original perceptron
Building a feedforward neural network from scratch
Applying the FNN XOR solution to a case study to optimize subsets of data
Summary
Questions
Further reading
5
Manage the Power of Machine Learning and Deep Learning
Manage the Power of Machine Learning and Deep Learning
Technical requirements
Building the architecture of an FNN with TensorFlow
Using TensorBoard to design the architecture of your machine learning and deep learning solutions
Using TensorBoard in a corporate environment
Summary
Questions
Further reading
References
6
Don't Get Lost in Techniques – Focus on Optimizing Your Solutions
Don't Get Lost in Techniques – Focus on Optimizing Your Solutions
Technical requirements
Dataset optimization and control
Implementing a k-means clustering solution
Summary
Questions
Further reading
7
When and How to Use Artificial Intelligence
When and How to Use Artificial Intelligence
Technical requirements
Checking whether AI can be avoided
Cloud solutions – AWS
Summary
Questions
Further reading
8
Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies
Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies
Technical requirements
Is AI disruptive?
Discover a world of opportunities with Google Translate
AI as a new frontier
Summary
Questions
Further reading
9
Getting Your Neurons to Work
Getting Your Neurons to Work
Technical requirements
Defining a CNN
Training a CNN model
Summary
Questions
Further reading and references
10
Applying Biomimicking to Artificial Intelligence
Applying Biomimicking to Artificial Intelligence
Technical requirements
Human biomimicking
Summary
Questions
Further reading
11
Conceptual Representation Learning
Conceptual Representation Learning
Technical requirements
Generate profit with transfer learning
Domain learning
Summary
Questions
Further reading
12
Automated Planning and Scheduling
Automated Planning and Scheduling
Technical requirements
Planning and scheduling today and tomorrow
CRLMM applied to an automated apparel manufacturing process
Building the DQN-CRLMM
Summary
Questions
Further reading
13
AI and the Internet of Things (IoT)
AI and the Internet of Things (IoT)
Technical requirements
The Iotham City project
Setting up the DQN-CRLMM model
Adding an SVM function
Running the CRLMM
Summary
Questions
Further reading
References
14
Optimizing Blockchains with AI
Optimizing Blockchains with AI
Technical requirements
Blockchain technology background
Using naive Bayes in a blockchain process
Implementation
Summary
Questions
Further reading
15
Cognitive NLP Chatbots
Cognitive NLP Chatbots
Technical requirements
IBM Watson
A cognitive chatbot service
Summary
Questions
Further reading
16
Improve the Emotional Intelligence Deficiencies of Chatbots
Improve the Emotional Intelligence Deficiencies of Chatbots
Technical requirements
Building a mind
Restricted Boltzmann Machines
Sentiment analysis
Conceptual representation learning meta-models
RNN for data augmentation
Word embedding
Principal component analysis
Summary
Questions
Further reading
17
Quantum Computers That Think
Quantum Computers That Think
Technical requirements
The rising power of quantum computers
A thinking quantum computer
Summary
Questions
Further reading
18
Answers to the Questions
Answers to the Questions
Chapter 1 – Become an Adaptive Thinker
Chapter 2 – Think like a Machine
Chapter 3 – Apply Machine Thinking to a Human Problem
Chapter 4 – Become an Unconventional Innovator
Chapter 5 – Manage the Power of Machine Learning and Deep Learning
Chapter 6 – Don't Get Lost in Techniques, Focus on Optimizing Your Solutions
Chapter 7 – When and How to Use Artificial Intelligence
Chapter 8 – Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies
Chapter 9 – Getting Your Neurons to Work
Chapter 10 – Applying Biomimicking to AI
Chapter 11 – Conceptual Representation Learning
Chapter 12 – Automated Planning and Scheduling
Chapter 13 – AI and the Internet of Things
Chapter 14 – Optimizing Blockchains with AI
Chapter 15 – Cognitive NLP Chatbots
Chapter 16 – Improve the Emotional Intelligence Deficiencies of Chatbots
Chapter 17 – Quantum Computers That Think

Questions

  1. A CNN can only process images. (Yes | No)
  2. A kernel is a preset matrix used for convolutions. (Yes | No)
  3. Does pooling have a pooling matrix or is it random?
  4. The size of the dataset always has to be large. (Yes | No)
  5. Finding a dataset is not a problem with all the available image banks on the web. (Yes | No)
  6. Once a CNN is built, training it does not take much time. (Yes | No)
  7. A trained CNN model applies to only one type of images. (Yes | No)
  8. A quadratic loss function is not very efficient compared to a cross-entropy function. (Yes | No)
  9. The performance of a deep learning CNN does not a represent a real issue with modern CPUs and GPUs. (Yes | No)
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End of Section 6
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