Artificial Intelligence By Example - Second Edition

Spis treści książki

• Preface
  • Who this book is for
  • What this book covers
  • To get the most out of this book
  • Get in touch
• 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
• 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
  • Summary
  • Questions
  • Further reading
• 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
  • Summary
  • Questions
  • Further reading
• 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
  • Summary
  • Questions
  • Further reading
• 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
  • Summary
  • Questions
  • Further reading
• 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 Googles 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
  • Summary
  • Questions
  • Further reading
• 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
  • Summary
  • Questions
  • Further reading
• 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
  • Summary
  • Questions
  • Further reading
• 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
      • Loading the data
    • Training with the classifier
    • Saving the model
      • Next steps
  • Summary
  • Questions
  • Further reading and references
• 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
  • Summary
  • Questions
  • Further reading
• 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
  • Summary
  • Questions
  • Further reading
• 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
  • Summary
  • Questions
  • Further reading
• 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
  • Summary
  • Questions
  • Further reading
• Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA)
  • Defining basic terms and goals
  • Introducing and building an RBM
    • The architecture of an RBM
    • An energy-based model
    • Building the RBM in Python
      • Creating a class and the structure of the RBM
      • Creating a training function in the RBM class
      • Computing the hidden units in the training function
      • Random sampling of the hidden units for the reconstruction and contractive divergence
      • Reconstruction
      • Contrastive divergence
      • Error and energy function
    • Running the epochs and analyzing the results
  • Using the weights of an RBM as feature vectors for PCA
    • Understanding PCA
      • Mathematical explanation
    • Using TensorFlow's Embedding Projector to represent PCA
    • Analyzing the PCA to obtain input entry points for a chatbot
  • Summary
  • Questions
  • Further reading
• Setting Up a Cognitive NLP UI/CUI Chatbot
  • Basic concepts
    • Defining NLU
    • Why do we call chatbots "agents"?
    • Creating an agent to understand Dialogflow
    • Entities
    • Intents
    • Context
  • Adding fulfillment functionality to an agent
    • Defining fulfillment
    • Enhancing the cogfilmdr agent with a fulfillment webhook
    • Getting the bot to work on your website
  • Machine learning agents
    • Using machine learning in a chatbot
    • Speech-to-text
    • Text-to-speech
    • Spelling
    • Why are these machine learning algorithms important?
  • Summary
  • Questions
  • Further reading
• Improving the Emotional Intelligence Deficiencies of Chatbots
  • From reacting to emotions, to creating emotions
    • Solving the problems of emotional polysemy
      • The greetings problem example
      • The affirmation example
      • The speech recognition fallacy
      • The facial analysis fallacy
    • Small talk
      • Courtesy
      • Emotions
  • Data logging
  • Creating emotions
  • RNN research for future automatic dialog generation
    • RNNs at work
      • RNN, LSTM, and vanishing gradients
    • Text generation with an RNN
    • Vectorizing the text
    • Building the model
    • Generating text
  • Summary
  • Questions
  • Further reading
• Genetic Algorithms in Hybrid Neural Networks
  • Understanding evolutionary algorithms
    • Heredity in humans
      • Our cells
      • How heredity works
    • Evolutionary algorithms
      • Going from a biological model to an algorithm
      • Basic concepts
    • Building a genetic algorithm in Python
      • Importing the libraries
      • Calling the algorithm
      • The main function
      • The parent generation process
      • Generating a parent
      • Fitness
      • Display parent
      • Crossover and mutation
      • Producing generations of children
      • Summary code
    • Unspecified target to optimize the architecture of a neural network with a genetic algorithm
      • A physical neural network
      • What is the nature of this mysterious S-FNN?
      • Calling the algorithm cell
      • Fitness cell
      • ga_main() cell
  • Artificial hybrid neural networks
    • Building the LSTM
    • The goal of the model
  • Summary
  • Questions
  • Further reading
• Neuromorphic Computing
  • Neuromorphic computing
  • Getting started with Nengo
    • Installing Nengo and Nengo GUI
    • Creating a Python program
    • A Nengo ensemble
      • Nengo neuron types
      • Nengo neuron dimensions
      • A Nengo node
    • Connecting Nengo objects
    • Visualizing data
    • Probes
  • Applying Nengo's unique approach to critical AI research areas
  • Summary
  • Questions
  • References
  • Further reading
• Quantum Computing
  • The rising power of quantum computers
    • Quantum computer speed
    • Defining a qubit
    • Representing a qubit
    • The position of a qubit
      • Radians, degrees, and rotations
      • The 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
    • The MindX experiment
      • Preparing the data
      • Transformation functions the situation function
      • Transformation functions the quantum function
      • Creating and running the score
      • Using the output
  • Summary
  • Questions
  • Further reading
• Answers to the Questions
  • Chapter 1 Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning
  • Chapter 2 Building a Reward Matrix Designing Your Datasets
  • Chapter 3 Machine Intelligence Evaluation Functions and Numerical Convergence
  • Chapter 4 Optimizing Your Solutions with K-Means Clustering
  • Chapter 5 How to Use Decision Trees to Enhance K-Means Clustering
  • Chapter 6 Innovating AI with Google Translate
  • Chapter 7 Optimizing Blockchains with Naive Bayes
  • Chapter 8 Solving the XOR Problem with a Feedforward Neural Network
  • Chapter 9 Abstract Image Classification with Convolutional Neural Networks (CNNs)
  • Chapter 10 Conceptual Representation Learning
  • Chapter 11 Combining Reinforcement Learning and Deep Learning
  • Chapter 12 AI and the Internet of Things
  • Chapter 13 Visualizing Networks with TensorFlow 2.x and TensorBoard
  • Chapter 14 Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA)
  • Chapter 15 Setting Up a Cognitive NLP UI/CUI Chatbot
  • Chapter 16 Improving the Emotional Intelligence Deficiencies of Chatbots
  • Chapter 17 Genetic Algorithms in Hybrid Neural Networks
  • Chapter 18 Neuromorphic Computing
  • Chapter 19 Quantum Computing
• Other Books You May Enjoy
• Index

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