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Artificial Intelligence By Example
Artificial Intelligence By Example

Artificial Intelligence By Example: Acquire advanced AI, machine learning, and deep learning design skills , Second Edition

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Artificial Intelligence By Example

Building a Reward Matrix – Designing Your Datasets

Experimenting and implementation comprise the two main approaches of artificial intelligence. Experimenting largely entails trying ready-to-use datasets and black box, ready-to-use Python examples. Implementation involves preparing a dataset, developing preprocessing algorithms, and then choosing a model, the proper parameters, and hyperparameters.

Implementation usually involves white box work that entails knowing exactly how an algorithm works and even being able to modify it.

In Chapter 1, Getting Started with Next-Generation Artifcial Intelligence through Reinforcement Learning, the MDP-driven Bellman equation relied on a reward matrix. In this chapter, we will get our hands dirty in a white box process to create that reward matrix.

An MDP process cannot run without a reward matrix. The reward matrix determines whether it is possible to go from one cell to another, from A to B. It is like a map of a city that...

Designing datasets – where the dream stops and the hard work begins

As in the previous chapter, bear in mind that a real-life project goes through a three-dimensional method in some form or other. First, it's important to think and talk about the problem in need of solving without jumping onto a laptop. Once that is done, bear in mind that the foundation of machine learning and deep learning relies on mathematics. Finally, once the problem has been discussed and mathematically represented, it is time to develop the solution.

First, think of a problem in natural language. Then, makemathematical description of a problem. Only then should you begin the software implementation.

Designing datasets

The reinforcement learning program described in the first chapter can solve a variety of problems involving unlabeled classification in an unsupervised decision-making process. The Q function can be applied to drone, truck, or car deliveries. It...

Logistic activation functions and classifiers

Now that the value of each location of L = {l1, l2, l3, l4, l5, l6} contains its availability in a vector, the locations can be sorted from the most available to the least available location. From there, the reward matrix, R, for the MDP process described in Chapter 1, Getting Started with Next-Generation Artifcial Intelligence through Reinforcement Learning, can be built.

Overall architecture

At this point, the overall architecture contains two main components:

  1. Chapter 1: A reinforcement learning program based on the value-action Q function using a reward matrix that will be finalized in this chapter. The reward matrix was provided in the first chapter as an experiment, but in the implementation phase, you'll often have to build it from scratch. It sometimes takes weeks to produce a good reward matrix.
  2. Chapter 2: Designing a set of 6×1 neurons that represents the flow of products at a...

Summary

Using a McCulloch-Pitts neuron with a logistic activation function in a one-layer network to build a reward matrix for reinforcement learning shows one way to preprocess a dataset.

Processing real-life data often requires a generalization of a logistic sigmoid function through a softmax function, and a one-hot function applied to logits to encode the data.

Machine learning functions are tools that must be understood to be able to use all or parts of them to solve a problem. With this practical approach to artificial intelligence, a whole world of projects awaits you.

This neuronal approach is the parent of the multilayer perceptron that will be introduced starting in Chapter 8, Solving the XOR Problem with a Feedforward Neural Network.

This chapter went from an experimental black box machine learning and deep learning to white box implementation. Implementation requires a full understanding of machine learning algorithms that often require fine-tuning.

...

Questions

  1. Raw data can be the input to a neuron and transformed with weights. (Yes | No)
  2. Does a neuron require a threshold? (Yes | No)
  3. A logistic sigmoid activation function makes the sum of the weights larger. (Yes | No)
  4. A McCulloch-Pitts neuron sums the weights of its inputs. (Yes | No)
  5. A logistic sigmoid function is a log10 operation. (Yes | No)
  6. A logistic softmax is not necessary if a logistic sigmoid function is applied to a vector. (Yes | No)
  7. A probability is a value between –1 and 1. (Yes | No)

Further reading

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Key benefits

  • AI-based examples to guide you in designing and implementing machine intelligence
  • Build machine intelligence from scratch using artificial intelligence examples
  • Develop machine intelligence from scratch using real artificial intelligence

Description

AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, 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 Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing. By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions.

Who is this book for?

Developers and those interested in AI, who want to understand the fundamentals of Artificial Intelligence and implement them practically. Prior experience with Python programming and statistical knowledge is essential to make the most out of this book.

What you will learn

  • Apply k-nearest neighbors (KNN) to language translations and explore the opportunities in Google Translate
  • Understand chained algorithms combining unsupervised learning with decision trees
  • Solve the XOR problem with feedforward neural networks (FNN) and build its architecture to represent a data flow graph
  • Learn about meta learning models with hybrid neural networks
  • Create a chatbot and optimize its emotional intelligence deficiencies with tools such as Small Talk and data logging
  • Building conversational user interfaces (CUI) for chatbots
  • Writing genetic algorithms that optimize deep learning neural networks
  • Build quantum computing circuits

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Publication date : Feb 28, 2020
Length: 578 pages
Edition : 2nd
Language : English
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Length: 578 pages
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Language : English
ISBN-13 : 9781839211539
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Table of Contents

22 Chapters
Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning Chevron down icon Chevron up icon
Building a Reward Matrix – Designing Your Datasets Chevron down icon Chevron up icon
Machine Intelligence – Evaluation Functions and Numerical Convergence Chevron down icon Chevron up icon
Optimizing Your Solutions with K-Means Clustering Chevron down icon Chevron up icon
How to Use Decision Trees to Enhance K-Means Clustering Chevron down icon Chevron up icon
Innovating AI with Google Translate Chevron down icon Chevron up icon
Optimizing Blockchains with Naive Bayes Chevron down icon Chevron up icon
Solving the XOR Problem with a Feedforward Neural Network Chevron down icon Chevron up icon
Abstract Image Classification with Convolutional Neural Networks (CNNs) Chevron down icon Chevron up icon
Conceptual Representation Learning Chevron down icon Chevron up icon
Combining Reinforcement Learning and Deep Learning Chevron down icon Chevron up icon
AI and the Internet of Things (IoT) Chevron down icon Chevron up icon
Visualizing Networks with TensorFlow 2.x and TensorBoard Chevron down icon Chevron up icon
Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA) Chevron down icon Chevron up icon
Setting Up a Cognitive NLP UI/CUI Chatbot Chevron down icon Chevron up icon
Improving the Emotional Intelligence Deficiencies of Chatbots Chevron down icon Chevron up icon
Genetic Algorithms in Hybrid Neural Networks Chevron down icon Chevron up icon
Neuromorphic Computing Chevron down icon Chevron up icon
Quantum Computing Chevron down icon Chevron up icon
Answers to the Questions Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.6
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4 star 11.8%
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Tamzid Bhuiyan May 19, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
information are helpful , good for students
Amazon Verified review Amazon
Jean-patrice Glafkides Mar 28, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a big book that aim at showing all aspects of AI. It drives us trhu to almost all kind of AI as of now with sample. It helps me find quick information on some specific part of AI.
Amazon Verified review Amazon
Laura Mar 24, 2020
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This book covers a breath of concepts not typically found in a single resource. Valuable for understanding both the theory and the applications of AI development. Very insightful!
Amazon Verified review Amazon
Pedro V. Marcal Mar 30, 2020
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A long established mantra of AI is that 'data is program and program is data'. Nowhere is this more true than in Reinforcement Learning (RL) and Deep Learning (DL). Here there is no established theory, just established Algorithms. The author's book steps in and by careful building of examples illustrates the underlying principles behind RL and DL. Each example is explained by [1] explaining the basis of the required data. [2] a textual understanding of the problem. [3] the development of a Python based program to solve the problem.I recommend this book to those who wish to gain a better insight into RL and DL and are willing to work towards it.
Amazon Verified review Amazon
user Apr 03, 2020
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There is only one way to learn data science: With a solid understanding of the underlying theory, and by getting your hands dirty with real examples, data, and code. AI by example delivers exactly this mix, with examples that go beyond contrived toy boxes, and real code on git.While I wish the book had a chapter how to assure ML by scrutinizing data, debugging models, assessing model competence, and how to apply general software engineering principles as algorithms are derived from data, I understand that these issues concern a more experienced audience and would be out of scope for beginners. Still, I can see this book as a reference even for more experienced professionals, and it would be great to see these aspects considered in a third edition.That said, this book is equally for developers who need to learn how to build solid machine learning programs, project managers and consultants need to learn what ML is and is not capable of, as well as teachers and students who get a comprehensive overview of the key aspects of AI, along with their first hands-on experience training models.
Amazon Verified review Amazon
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