Artificial Intelligence By Example: Acquire Advanced AI, Machine Learning and Deep Learning design skills

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Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examples. 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 About Artificial intelligence (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. Features 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 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 Summary Questions Further reading 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 Summary Questions Further reading 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 Summary Questions Further reading 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 Summary Questions Further reading 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 Summary Questions Further reading 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 Summary Questions Further reading 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 Summary Questions Further reading 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 Loading the data Training with the classifier Saving the model Next steps Summary Questions 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 Summary Questions Further reading 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 Summary Questions Further reading 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 Summary Questions Further reading 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 Summary Questions Further reading Chapter 14: 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 Chapter 15: 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 Chapter 16: Improve 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 Chapter 17: 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 Chapter 18: 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 Chapter 19: 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 Appendix: 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 – Improve 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