Artificial Intelligence By Example: Acquire advanced AI, machine learning, and deep learning design skills, 2nd Edition

Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examplesKey FeaturesAI-based examples to guide you in designing and implementing machine intelligenceBuild machine intelligence from scratch using artificial intelligence examplesDevelop machine intelligence from scratch using real artificial intelligenceBook DescriptionAI 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.What you will learnApply k-nearest neighbors (KNN) to language translations and explore the opportunities in Google TranslateUnderstand chained algorithms combining unsupervised learning with decision treesSolve the XOR problem with feedforward neural networks (FNN) and build its architecture to represent a data flow graphLearn about meta learning models with hybrid neural networksCreate a chatbot and optimize its emotional intelligence deficiencies with tools such as Small Talk and data loggingBuilding conversational user interfaces (CUI) for chatbotsWriting genetic algorithms that optimize deep learning neural networksBuild quantum computing circuitsWho this book is forDevelopers 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.Table of ContentsGetting Started with Next-Generation Artificial Intelligence through Reinforcement LearningBuilding a Reward Matrix – Designing Your DatasetsMachine Intelligence – Evaluation Functions and Numerical ConvergenceOptimizing Your Solutions with K-Means ClusteringHow to Use Decision Trees to Enhance K-Means ClusteringInnovating AI with Google TranslateOptimizing Blockchains with Naive BayesSolving the XOR Problem with a FNNAbstract Image Classification with CNNConceptual Representation LearningCombining RL and DLAI and the IoTVisualizing Networks with TensorFlow 2.x and TensorBoardPreparing the Input of Chatbots with RBMs and PCASetting Up a Cognitive NLP UI/CUI ChatbotImproving the Emotional Intelligence Deficiencies of ChatbotsGenetic Algorithms in Hybrid Neural NetworksNeuromorphic ComputingQuantum ComputingAppendix - Answers to the Questions Read more