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Artificial Intelligence By Example
Develop machine intelligence from scratch using real artificial intelligence use cases
Product type
Paperback
Published in
May 2018
Publisher
Packt
ISBN-13
9781788990547
Length
490 pages
Edition
1st Edition
Languages
Python
Tools
TensorFlow
Concepts
Artificial Intelligence
Author (1):
Denis Rothman
Denis Rothman
Denis Rothman
Expert in AI Transformers including ChatGPT/GPT-4, Bestselling Author
Read more
View More author details
Table of Contents
(24) Chapters
Title Page
Dedication
Packt Upsell
Contributors
Preface
1. Become an Adaptive Thinker
FREE CHAPTER
Technical requirements
How to be an adaptive thinker
The lessons of reinforcement learning
Summary
Questions
Further reading
2. 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
Technical requirements
Determining what and how to measure
Applying machine thinking to a human problem
Summary
Questions
Further reading
4. 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
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
Technical requirements
Dataset optimization and control
Implementing a k-means clustering solution
Summary
Questions
Further reading
7. 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
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
Technical requirements
Defining a CNN
Training a CNN model
Summary
Questions
Further reading and references
10. Applying Biomimicking to Artificial Intelligence
Technical requirements
Human biomimicking
Summary
Questions
Further reading
11. Conceptual Representation Learning
Technical requirements
Generate profit with transfer learning
Domain learning
Summary
Questions
Further reading
12. 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)
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
Technical requirements
Blockchain technology background
Using naive Bayes in a blockchain process
Implementation
Summary
Questions
Further reading
15. Cognitive NLP Chatbots
Technical requirements
IBM Watson
A cognitive chatbot service
Summary
Questions
Further reading
16. 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
Technical requirements
The rising power of quantum computers
A thinking quantum computer
Summary
Questions
Further reading
1. 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
Index
Further reading
Further information on Amazon Web Services, Sagemaker, and k-means clustering can be found here:
https://docs.aws.amazon.com/sagemaker/latest/dg/k-means.html
https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/amazon/kmeans.py
Further information on k-means clustering can be found here:
https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf
https://docs.aws.amazon.com/sagemaker/latest/dg/algo-kmeans-tech-notes.html
https://pdfs.semanticscholar.org/0074/4cb7cc9ccbbcdadbd5ff2f2fee6358427271.pdf
Further information on MXNET can be found here:
https://mxnet.incubator.apache.org/architecture/overview.html
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Authors (1)
Denis Rothman
Denis Rothman
Expert in AI Transformers including ChatGPT/GPT-4, Bestselling Author
Read more
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