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TABLE OF CONTENTS15 sections

Basics Deep Learning and Neural Networks

01Basics Deep Learning and Neural Networks 02Artificial Intelligence Machine Learning And Deep Learning 03Mathematical And Computational Foundations 04Artificial Neurons And Network Architecture 05Forward Propagation And Representation Learning 06Loss Functions Backpropagation And Optimization 07Data Preparation And Deep Learning Workflow 08Model Training Regularization And Hyperparameter Tuning 09Evaluation Experimentation And Error Analysis 10Convolutional Neural Networks 11Sequence Models And Natural Language Processing 12Attention Transformers And Foundation Models 13Deep Learning Tools And Practical Model Development 14Deployment Mlops And Organizational Applications 15Responsible Ai Governance And Future Directions

Chapter 2

Mathematical And Computational Foundations

PREVIOUS SECTIONArtificial Intelligence Machine Learning And... UP NEXTArtificial Neurons And Network Architecture

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