Artificial Intelligence By Example by Rothman, Denis -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Artificial Intelligence By Example

HumbleBundle Dedication Packt Upsell

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Contributors

About the author About the reviewers Packt is searching for authors like you

Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Download the color images Code in Action Conventions used

Get in touch

Reviews

Become an Adaptive Thinker

Technical requirements How to be an adaptive thinker

Addressing real-life issues before coding a solution

Step 1 – MDP in natural language Step 2 – the mathematical representation of the Bellman equation and MDP

From MDP to the Bellman equation

Step 3 – implementing the solution in Python

The lessons of reinforcement learning

How to use the outputs Machine learning versus traditional applications

Summary Questions Further reading

Think like a Machine

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

Designing datasets in natural language meetings Using the McCulloch-Pitts neuron The McCulloch-Pitts neuron The architecture of Python TensorFlow

Logistic activation functions and classifiers

Overall architecture Logistic classifier Logistic function Softmax

Summary Questions Further reading

Apply Machine Thinking to a Human Problem

Technical requirements Determining what and how to measure

Convergence

Implicit convergence Numerical – controlled convergence

Applying machine thinking to a human problem

Evaluating a position in a chess game Applying the evaluation and convergence process to a business problem Using supervised learning to evaluate result quality

Summary Questions Further reading

Become an Unconventional Innovator

Technical requirements The XOR limit of the original perceptron

XOR and linearly separable models

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

Building a feedforward neural network from scratch

Step 1 – Defining a feedforward neural network Step 2 – how two children 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 solution to a case study to optimize subsets of data Summary Questions Further reading

Manage the Power of Machine Learning and Deep Learning

Technical requirements Building the architecture of an FNN with TensorFlow

Writing code using the data flow graph as an architectural roadmap

A data flow graph translated into source code

The input data layer The hidden layer The output layer The cost or loss function Gradient descent and backpropagation Running the session Checking linear separability

Using TensorBoard to design the architecture of your machine learning and deep learning solutions

Designing the architecture of the data flow graph Displaying the data flow graph in TensorBoard The final source code with TensorFlow and TensorBoard

Using TensorBoard in a corporate environment

Using TensorBoard to explain the concept of classifying customer products to a CEO Will your views on the project survive this meeting?

Summary Questions Further reading References

Don't Get Lost in Techniques – Focus on Optimizing Your Solutions

Technical requirements Dataset optimization and control

Designing a dataset and choosing an ML/DL model

Approval of the design matrix

Agreeing on the format of the design matrix Dimensionality reduction The volume of a training dataset

Implementing a k-means clustering solution

The vision

The data

Conditioning management

The strategy

The k-means clustering program

The mathematical definition of k-means clustering

Lloyd's algorithm

The goal of k-means clustering in this case study The Python program

1 – The training dataset 2 – Hyperparameters 3 – The k-means clustering algorithm 4 – Defining the result labels 5 – Displaying the results – data points and clusters Test dataset and prediction

Analyzing and presenting the results

AGV virtual clusters as a solution

Summary Questions Further reading

When and How to Use Artificial Intelligence

Technical requirements Checking whether AI can be avoided

Data volume and applying k-means clustering

Proving your point

NP-hard – the meaning of P NP-hard – The meaning of non-deterministic The meaning of hard

Random sampling

The law of large numbers – LLN The central limit theorem

Using a Monte Carlo estimator

Random sampling applications

Cloud solutions – AWS

Preparing your baseline model

Training the full sample training dataset Training a random sample of the training dataset Shuffling as an alternative to random sampling

AWS – data management

Buckets Uploading files Access to output results

SageMaker notebook Creating a job Running a job

Reading the results

Recommended strategy

Summary Questions Further reading

Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies

Technical requirements Is AI disruptive?

What is new and what isn't in AI

AI is based on mathematical theories that are not new Neural networks are not new Cloud server power, data volumes, and web sharing of the early 21st century started to make AI disruptive Public awareness contributed to making AI disruptive

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

Lexical field theory Jargon Translating is not just translating but interpreting How to check a translation

AI as a new frontier

Lexical field and polysemy Exploring the frontier – the program

k-nearest neighbor algorithm

The KNN algorithm The knn_polysemy.py program Implementing the KNN compressed function in Google_Translate_Customized.py Conclusions on the Google Translate customized experiment

The disruptive revolutionary loop

Summary Questions Further reading

Getting Your Neurons to Work

Technical requirements Defining a CNN

Defining a CNN Initializing the CNN Adding a 2D convolution

Kernel

Intuitive approach Developers' approach Mathematical approach

Shape ReLu

Pooling Next convolution and pooling layer Flattening Dense layers

Dense activation functions

Training a CNN model

The goal Compiling the model

Loss function

Quadratic loss function Binary cross-entropy

Adam optimizer Metrics

Training dataset

Data augmentation Loading the data

Testing dataset

Data augmentation Loading the data

Training with the classifier Saving the model

Next steps

Summary Questions Further reading and references

Applying Biomimicking to Artificial Intelligence

Technical requirements Human biomimicking

TensorFlow, an open source machine learning framework

Does deep learning represent our brain or our mind?

A TensorBoard representation of our mind

Input data Layer 1 – managing the inputs to the network

Weights, biases, and preactivation Displaying the details of the activation function through the preactivation process The activation function of Layer 1

Dropout and Layer 2 Layer 2 Measuring the precision of prediction of a network through accuracy values

Correct prediction accuracy

Cross-entropy Training

Optimizing speed with Google's Tensor Processing Unit

Summary Questions Further reading

Conceptual Representation Learning

Technical requirements Generate profit with transfer learning

The motivation of transfer learning

Inductive thinking Inductive abstraction The problem AI needs to solve

The Γ gap concept Loading the Keras model after training

Loading the model to optimize training Loading the model to use it

Using transfer learning to be profitable or see a project stopped

Defining the strategy

Applying the model Making the model profitable by using it for another problem

Where transfer learning ends and domain learning begins

Domain learning

How to use the programs

The trained models used in this section The training model program

GAP – loaded or unloaded GAP – jammed or open lanes The gap dataset

Generalizing the Γ(gap conceptual dataset) Generative adversarial networks

Generating conceptual representations The use of autoencoders

The motivation of conceptual representation learning meta-models

The curse of dimensionality The blessing of dimensionality

Scheduling and blockchains Chatbots Self-driving cars

Summary Questions Further reading

Automated Planning and Scheduling

Technical requirements 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 pγ and negative nγ gaps Apparel conveyor belt processing – undetermined gaps The beginning of an abstract notion of gaps

Modifying the hyperparameters Running a prediction program

Building the DQN-CRLMM

A circular process Implementing a CNN-CRLMM to detect gaps and optimize Q-Learning – MDP

MDP inputs and outputs

The input is a neutral reward matrix The standard output of the MDP function A graph interpretation of the MDP output matrix

The optimizer

The optimizer as a regulator

Implementing Z – squashing the MDP result matrix Implementing Z – squashing the vertex weights vector

Finding the main target for the MDP function

Circular DQN-CRLMM – a stream-like system that never starts nor ends

Summary Questions Further reading

AI and the Internet of Things (IoT)

Technical requirements The Iotham City project Setting up the DQN-CRLMM model

Training the CRLMM

The dataset Training and testing the model

Classifying the parking lots

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

Summary Questions Further reading References

Optimizing Blockchains with AI

Technical requirements Blockchain technology background

Mining bitcoins Using cryptocurrency Using blockchains

Using blockchains in the A-F network

Creating a block Exploring the blocks

Using naive Bayes in a blockchain process

A naive Bayes example

The blockchain anticipation novelty The goal

Step 1 the dataset Step 2 frequency Step 3 likelihood Step 4 naive Bayes equation

Implementation

Gaussian naive Bayes

The Python program

Implementing your ideas

Summary Questions Further reading

Cognitive NLP Chatbots

Technical requirements IBM Watson

Intents

Testing the subsets

Entities Dialog flow

Scripting and building up the model

Adding services to a chatbot

A cognitive chatbot service

The case study

A cognitive dataset

Cognitive natural language processing

Activating an image + word cognitive chat Solving the problem

Implementation

Summary Questions Further reading

Improve the Emotional Intelligence Deficiencies of Chatbots

Technical requirements Building a mind

How to read this chapter The profiling scenario

Restricted Boltzmann Machines

The connections between visible and hidden units Energy-based models

Gibbs random sampling Running the epochs and analyzing the results

Sentiment analysis

Parsing the datasets

Conceptual representation learning meta-models

Profiling with images

RNN for data augmentation

RNNs and LSTMs

RNN, LSTM, and vanishing gradients

Prediction as data augmentation

Step1 – providing an input file Step 2 – running an RNN Step 3 – producing data augmentation

Word embedding

The Word2vec model

Principal component analysis

Intuitive explanation Mathematical explanation

Variance Covariance Eigenvalues and eigenvectors Creating the feature vector Deriving the dataset Summing it up

TensorBoard Projector

Using Jacobian matrices

Summary Questions Further reading

Quantum Computers That Think

Technical requirements The rising power of quantum computers

Quantum computer speed Defining a qubit

Representing a qubit

The position of a qubit

Radians, degrees, and rotations Bloch sphere

Composing a quantum score

Quantum gates with Quirk A quantum computer score with Quirk A quantum computer score with IBM Q

A thinking quantum computer

Representing our mind's concepts Expanding MindX's conceptual representations Concepts in the mind-dataset of MindX

Positive thinking Negative thinking Gaps Distances

The embedding program The MindX experiment

Preparing the data Transformation Functions – the situation function Transformation functions – the quantum function Creating and running the score Using the output IBM Watson and scripts

Summary Questions Further reading

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

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