دانلود کتاب Deep Learning with Python: Learn Best Practices of Deep Learning Models with PyTorch

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Table of Contents About the Authors About the Technical Reviewers Acknowledgments Introduction Chapter 1: Introduction to Machine Learning and Deep Learning Defining Deep Learning A Brief History Rule-Based Systems Knowledge-Based Systems Machine Learning Deep Learning Advances in Related Fields Prerequisites The Approach Ahead Installing the Required Libraries The Concept of Machine Learning Binary Classification Regression Generalization Regularization Summary Chapter 2: Introduction to PyTorch Why Do We Need a Deep Learning Framework? What Is PyTorch? Why PyTorch? It All Starts with a Tensor Creating Tensors Tensor Munging Operations Mathematical Operations Element-Wise Mathematical Operations Trigonometric Operations in Tensors Comparison Operations for Tensors Linear Algebraic Operations Summary Chapter 3: Feed-Forward Neural Networks What Is a Neural Network? Unit The Overall Structure of a Neural Network Expressing a Neural Network in Vector Form Evaluating the Output of a Neural Network Training a Neural Network Deriving Cost Functions Using Maximum Likelihood Binary Cross-Entropy Cross-Entropy Squared Error Summary of Loss Functions Types of Activation Functions Linear Unit Sigmoid Activation Softmax Activation Rectified Linear Unit Hyperbolic Tangent Backpropagation Gradient Descent Variants Batch Gradient Descent Stochastic Gradient Descent Mini-Batch Gradient Descent Gradient-Based Optimization Techniques Gradient Descent with Momentum RMSprop Adam Practical Implementation with PyTorch Summary Chapter 4: Automatic Differentiation in Deep Learning Numerical Differentiation Symbolic Differentiation Automatic Differentiation Fundamentals Implementing Automatic Differentiation What Is Autograd? Summary Chapter 5: Training Deep Leaning Models Performance Metrics Classification Metrics Regression Metrics Mean Squared Error Mean Absolute Error Mean Absolute Percentage Error Data Procurement Splitting Data for Training, Validation, and Testing Establishing the Achievable Limit on the Error Rate Establishing the Baseline with Standard Choices Building an Automated, End-to-End Pipeline Orchestration for Visibility Analysis of Overfitting and Underfitting Hyperparameter Tuning Model Capacity Regularizing the Model Early Stopping Norm Penalties Dropout A Practical Implementation in PyTorch Interpreting the Business Outcomes for Deep Learning Summary Chapter 6: Convolutional Neural Networks Convolution Operation Pooling Operation Convolution-Detector-Pooling Building Block Stride Padding Batch Normalization Filter Filter Depth Number of Filters Summarizing key learnings from CNNs Implementing a basic CNN using PyTorch Implementing a larger CNN in PyTorch CNN Thumb Rules Summary Chapter 7: Recurrent Neural Networks Introduction to RNNs Training RNNs Bidirectional RNNs Vanishing and Exploding Gradients Gradient Clipping Long Short-Term Memory Practical Implementation Summary Chapter 8: Recent Advances in Deep Learning Going Beyond Classification in Computer Vision Object Detection Image Segmentation Pose Estimation Generative Computer Vision Natural Language Processing with Deep Learning Transformer Models Bidirectional Encoder Representations from Transformers GrokNet Additional Noteworthy Research Concluding Thoughts Index