book

AI Revealed

Name: AI Revealed
Author: Herman Erik
ISBN: 9781501520679

by Herman Erik

November 2024

Beginner to intermediate

194 pages

3h 55m

English

Mercury Learning and Information

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Cover
Half title
Title
Copyright
Dedication
Contents
Preface
Chapter 1: The Foundations of Artificial Intelligence
What is Artificial Intelligence?Definition of AITypes of AINarrow AIGeneral AIArtificial SuperintelligenceCore Components of AI SystemsMachine Learning (ML)Neural NetworksRoboticsExpert SystemsThe History of AIEarly Concepts and TheoriesKey MilestonesModern DevelopmentsImportance and Applications of AITransformational ImpactHealthcareAutomotiveFinanceCustomer ServiceDaily LifeEthical ConsiderationsFundamental QuestionsBias and FairnessPrivacyRegulation and GovernanceAI Application: Create a Simple Rule-Based ChatbotStep 1: Set Up the Development EnvironmentStep 2: Create the Chatbot ScriptStep 3: Run the ChatbotStep 4: Understand the ScriptConclusion
Chapter 2: Foundations of Machine Learning
Introduction to Machine LearningDefinition and ScopeHow ML WorksKey ComponentsDataModelLearning AlgorithmEvaluation MetricsSupervised LearningConcept and MechanismCommon AlgorithmsLinear RegressionLogistic RegressionDecision TreesSupport Vector Machines (SVMs)ApplicationsSpam DetectionSentiment AnalysisCredit Scoring SystemsMedical DiagnosisFraud DetectionPredictive MaintenanceCustomer Churn PredictionStock Market PredictionUnsupervised LearningConcept and MechanismCommon AlgorithmsClustering AlgorithmsAssociation AlgorithmsDimensionality Reduction TechniquesApplicationsCustomer SegmentationMarket Basket AnalysisAnomaly DetectionSocial Network AnalysisDocument ClusteringImage CompressionBioinformaticsModel Evaluation and SelectionEvaluation MetricsPrecision and RecallThe F1 ScoreMean Squared Error (MSE) and Root Mean Squared Error (RMSE)Area Under the Receiver Operating Characteristic Curve (AUC-ROC)Validation TechniquesCross-ValidationBootstrappingModel SelectionAI Application: Implement a Linear Regression Model to Predict House PricesStep 1: Set Up the Development EnvironmentStep 2: Prepare the DatasetStep 3: Implement the Linear Regression ModelStep 4: Run the ScriptConclusion
Chapter 3: Deep Learning and Neural Networks
Introduction to Deep LearningUnderstanding Neural NetworksBasic StructureHow They LearnTypes of Neural NetworksFeedforward Neural NetworksRecurrent Neural Networks (RNNs)Convolutional Neural Networks (CNNs)TransformersKey Architectures in Deep LearningCNNsRNNsTransformersChallenges and Ethical ConsiderationsComputational DemandsData RequirementsPrivacy and SecurityArtificial Neural Networks (ANNs)Fundamentals of ANNsNeuron ModelLayers of an ANNActivation FunctionsTraining Neural NetworksBackpropagationLoss FunctionsOptimization AlgorithmsPractical Applications of ANNsFinanceHealthcareIndustrial AutomationCNNsArchitecture of CNNsConvolutional LayersPooling LayersFully Connected LayersFunctionality and Training of CNNsFeature LearningBackpropagation in CNNsAdvanced Training TechniquesReal-World Applications of CNNsFacial Recognition SystemsMedical ImagingAutomotive IndustryRNNsHandling Sequential DataAdvanced ArchitecturesApplications of RNNsLanguage Translation ServicesVoice-Activated AssistantsFinancial ForecastingAdvanced Architectures (for example, GANs, Transformers)GANsGeneratorDiscriminatorTraining ProcessApplicationsTransformersCore MechanismTraining EfficiencyApplicationsAI Application: Build a Basic Neural Network for Digit Classification Using MNIST DatasetStep 1: Set Up the Development EnvironmentStep 2: Load and Preprocess the MNIST DatasetStep 3: Create the Neural NetworkStep 4: Train the Neural NetworkStep 5: Evaluate the ModelStep 6: Run the ScriptConclusion

Chapter 4: Natural Language Processing (NLP)
Introduction to NLPFundamentals of NLPSyntaxSemanticsPragmaticsTechniques in NLPText PreprocessingParsing and Part-of-Speech TaggingMachine Learning (ML) in NLPChallenges in NLPAmbiguity and ContextSlang and DialectsResource AvailabilityReal-World Applications of NLPVoice-Activated AssistantsCustomer Service BotsAutomated Translation ServicesText PreprocessingTokenizationStemmingLemmatizationRemoving Stop WordsSentiment AnalysisNamed Entity RecognitionMachine TranslationAI Application: Perform Sentiment Analysis on a Set of Movie ReviewsStep 1: Set Up Development EnvironmentStep 2: Load and Preprocess the Movie Reviews DatasetStep 3: Train the Sentiment Analysis ModelStep 4: Evaluate the ModelStep 5: Run the ScriptConclusion
Chapter 5: Computer Vision
Introduction to Computer VisionImage PreprocessingGrayscale ConversionHistogram EqualizationNormalizationEdge DetectionObject DetectionRegion-Based Convolutional Neural Networks (R-CNNs)YOLO (You Only Look Once)SSD (Single Shot Multidetector)Image ClassificationConvolutional Neural Networks (CNNs)Machine Learning AlgorithmsImage SegmentationThresholdingClustering MethodsAdvanced MethodsAI Application: Implement an Image Classification Model Using CIFAR-10 DatasetStep 1: Set Up Development EnvironmentStep 2: Load and Preprocess the CIFAR-10 DatasetStep 3: Build the Image Classification ModelStep 4: Train the Image Classification ModelStep 5: Evaluate the ModelStep 6: Run the ScriptConclusion
Chapter 6: Ethics and Bias in AI
Ethical Considerations in AIBias in AI AlgorithmsFairness and AccountabilityRegulation and GovernanceAI Application: Analyze Bias in a Dataset and Discuss Mitigation StrategiesStep 1: Set Up Development EnvironmentStep 2: Load and Explore the DatasetStep 3: Preprocess the DataStep 4: Train a Baseline ModelStep 5: Analyze Bias in the ModelStep 6: Mitigate BiasStep 7: Run the ScriptConclusion
Chapter 7: AI in Practice: Industry Case Studies
HealthcareFinanceTransportationRetailManufacturingAI Application: Predicting Patient Outcomes in HealthcareStep 1: Set Up Development EnvironmentStep 2: Load and Explore the Healthcare DatasetStep 3: Preprocess the DataStep 4: Train a Predictive ModelStep 5: Evaluate the ModelConclusion
Chapter 8: Future of AI and Emerging Technologies
Quantum ComputingEdge AIExplainable AIAI for Social GoodAI Application: Experiment With a Simple Quantum Computing Algorithm Using IBM’s QiskitStep 1: Set Up Development EnvironmentStep 2: Introduction to Quantum Computing BasicsStep 3: Implement a Basic Quantum AlgorithmConclusion
Chapter 9: Getting Started With AI Development
Setting Up Development EnvironmentIntroduction to Python for AIUsing Popular AI LibrariesAI Application: Set Up an AI Development Environment and Run a Basic Python ScriptStep 1: Install PythonStep 2: Install Jupyter NotebookStep 3: Set Up a Virtual Environment (optional but recommended)Step 4: Create and Run a Jupyter NotebookConclusion
Appendix A: Overview of the Lisp Programming Language
Appendix B: Resources and Community
Index

Content preview from AI Revealed

APPENDIX A

OVERVIEW OF THE LISP PROGRAMMING LANGUAGE

Lisp, short for “List Processing,” is one of the oldest high-level programming languages and has had a profound impact on the development of artificial intelligence (AI) and computer science. Created by John McCarthy in 1958, Lisp was designed to facilitate symbolic computation, which is essential for AI research. Its unique features and powerful capabilities have made it a staple in AI and many other fields.

KEY FEATURES OF LISP

Symbolic Expression (S-Expressions)

Lisp’s primary data structure is the symbolic expression, or S-expression, which can represent both code and data. This uniform representation simplifies the manipulation of code and data, making it easier to write programs that ...

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ISBN: 9781501520631

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AI Revealed

by Herman Erik

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