Artificial Intelligence by Example : Acquire Advanced AI, Machine Learning, and Deep Learning Design Skills.

Format:

Book

Author/Creator:

Rothman, Denis.

Language:

English

Subjects (All):

Artificial intelligence.

Physical Description:

1 online resource (579 pages)

Edition:

2nd ed.

Place of Publication:

Birmingham : Packt Publishing, Limited, 2020.

Biography/History:

Rothman Denis: Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an Advanced Planning and Scheduling (APS) solution used worldwide.

Summary:

Artificial Intelligence (AI) gets your system to think smart and learn intelligently. This book is packed with some of the smartest trending examples with which you will learn the fundamentals of AI. By the end, you will have acquired the basics of AI by practically applying the examples in this book.

Contents:

Cover

Copyright

Packt Page

Contributors

Table of Contents

Preface

Chapter 1: Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning

Reinforcement learning concepts

How to adapt to machine thinking and become an adaptive thinker

Overcoming real-life issues using the three-step approach

Step 1 - describing a problem to solve: MDP in natural language

Watching the MDP agent at work

Step 2 - building a mathematical model: the mathematical representation of the Bellman equation and MDP

From MDP to the Bellman equation

Step 3 - writing source code: implementing the solution in Python

The lessons of reinforcement learning

How to use the outputs

Possible use cases

Machine learning versus traditional applications

Summary

Questions

Further reading

Chapter 2: Building a Reward Matrix - Designing Your Datasets

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

Designing datasets

Using the McCulloch-Pitts neuron

The McCulloch-Pitts neuron

The Python-TensorFlow architecture

Logistic activation functions and classifiers

Overall architecture

Logistic classifier

Logistic function

Softmax

Chapter 3: Machine Intelligence - Evaluation Functions and Numerical Convergence

Tracking down what to measure and deciding how to measure it

Convergence

Implicit convergence

Numerically controlled gradient descent convergence

Evaluating beyond human analytic capacity

Using supervised learning to evaluate a result that surpasses human analytic capacity

Chapter 4: Optimizing Your Solutions with K-Means Clustering

Dataset optimization and control

Designing a dataset and choosing an ML/DL model.

Approval of the design matrix

Implementing a k-means clustering solution

The vision

The data

The strategy

The k-means clustering program

The mathematical definition of k-means clustering

The Python program

Saving and loading the model

Analyzing the results

Bot virtual clusters as a solution

The limits of the implementation of the k-means clustering algorithm

Chapter 5: How to Use Decision Trees to Enhance K-Means Clustering

Unsupervised learning with KMC with large datasets

Identifying the difficulty of the problem

NP-hard - the meaning of P

NP-hard - the meaning of non-deterministic

Implementing random sampling with mini-batches

Using the LLN

The CLT

Using a Monte Carlo estimator

Trying to train the full training dataset

Training a random sample of the training dataset

Shuffling as another way to perform random sampling

Chaining supervised learning to verify unsupervised learning

Preprocessing raw data

A pipeline of scripts and ML algorithms

Step 1 - training and exporting data from an unsupervised ML algorithm

Step 2 - training a decision tree

Step 3 - a continuous cycle of KMC chained to a decision tree

Random forests as an alternative to decision trees

Chapter 6: Innovating AI with Google Translate

Understanding innovation and disruption in AI

Is AI disruptive?

AI is based on mathematical theories that are not new

Neural networks are not new

Looking at disruption - the factors that are making AI disruptive

Cloud server power, data volumes, and web sharing of the early 21st century

Public awareness

Inventions versus innovations

Revolutionary versus disruptive solutions

Where to start?

Discover a world of opportunities with Google Translate.

Getting started

The program

The header

Implementing Google's translation service

Google Translate from a linguist's perspective

Playing with the tool

Linguistic assessment of Google Translate

AI as a new frontier

Lexical field and polysemy

Exploring the frontier - customizing Google Translate with a Python program

k-nearest neighbor algorithm

Implementing the KNN algorithm

The knn_polysemy.py program

Implementing the KNN function in Google_Translate_Customized.py

Conclusions on the Google Translate customized experiment

The disruptive revolutionary loop

Chapter 7: Optimizing Blockchains with Naive Bayes

Part I - the background to blockchain technology

Mining bitcoins

Using cryptocurrency

PART II - using blockchains to share information in a supply chain

Using blockchains in the supply chain network

Creating a block

Exploring the blocks

Part III - optimizing a supply chain with naive Bayes in a blockchain process

A naive Bayes example

The blockchain anticipation novelty

The goal - optimizing storage levels using blockchain data

Implementation of naive Bayes in Python

Gaussian naive Bayes

Chapter 8: Solving the XOR Problem with a Feedforward Neural Network

The original perceptron could not solve the XOR function

XOR and linearly separable models

Linearly separable models

The XOR limit of a linear model, such as the original perceptron

Building an FNN from scratch

Step 1 - defining an FNN

Step 2 - an example of how two children can solve the XOR problem every day

Implementing a vintage XOR solution in Python with an FNN and backpropagation

A simplified version of a cost function and gradient descent

Linear separability was achieved.

Applying the FNN XOR function to optimizing subsets of data

Chapter 9: Abstract Image Classification with Convolutional Neural Networks (CNNs)

Introducing CNNs

Defining a CNN

Initializing the CNN

Adding a 2D convolution layer

Kernel

Shape

ReLU

Pooling

Next convolution and pooling layer

Flattening

Dense layers

Dense activation functions

Training a CNN model

The goal

Compiling the model

The loss function

The Adam optimizer

Metrics

The training dataset

Data augmentation

Loading the data

The testing dataset

Data augmentation on the testing dataset

Training with the classifier

Saving the model

Next steps

Further reading and references

Chapter 10: Conceptual Representation Learning

Generating profit with transfer learning

The motivation behind transfer learning

Inductive thinking

Inductive abstraction

The problem AI needs to solve

The gap concept

Loading the trained TensorFlow 2.x model

Loading and displaying the model

Loading the model to use it

Defining a strategy

Making the model profitable by using it for another problem

Domain learning

How to use the programs

The trained models used in this section

The trained model program

Gap - loaded or underloaded

Gap - jammed or open lanes

Gap datasets and subsets

Generalizing the (the gap conceptual dataset)

The motivation of conceptual representation learning metamodels applied to dimensionality

The curse of dimensionality

The blessing of dimensionality

Chapter 11: Combining Reinforcement Learning and Deep Learning

Planning and scheduling today and tomorrow

A real-time manufacturing process.

Amazon must expand its services to face competition

A real-time manufacturing revolution

CRLMM applied to an automated apparel manufacturing process

An apparel manufacturing process

Training the CRLMM

Generalizing the unit training dataset

Food conveyor belt processing - positive p and negative n gaps

Running a prediction program

Building the RL-DL-CRLMM

A circular process

Implementing a CNN-CRLMM to detect gaps and optimize

Q-learning - MDP

MDP inputs and outputs

The optimizer

The optimizer as a regulator

Finding the main target for the MDP function

A circular model - a stream-like system that never starts nor ends

Chapter 12: AI and the Internet of Things (IoT)

The public service project

Setting up the RL-DL-CRLMM model

Applying the model of the CRLMM

The dataset

Using the trained model

Adding an SVM function

Motivation - using an SVM to increase safety levels

Definition of a support vector machine

Python function

Running the CRLMM

Finding a parking space

Deciding how to get to the parking lot

Support vector machine

The itinerary graph

The weight vector

Chapter 13: Visualizing Networks with TensorFlow 2.x and TensorBoard

Exploring the output of the layers of a CNN in two steps with TensorFlow

Building the layers of a CNN

Processing the visual output of the layers of a CNN

Analyzing the visual output of the layers of a CNN

Analyzing the accuracy of a CNN using TensorBoard

Getting started with Google Colaboratory

Defining and training the model

Introducing some of the measurements

Further reading.

Chapter 14: Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA).

Notes:

Previous edition published: 2018.

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

ISBN:

9781839212819

1839212810

OCLC:

1143622136