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Computer Science
Artificial Intelligence
Deep Learning
PyTorch Library
1. Introduction to PyTorch
2. Tensors: The Foundation
3. Tensor Operations and Manipulation
4. Automatic Differentiation
5. Neural Network Construction
6. Data Handling and Processing
7. Model Training and Optimization
8. Model Persistence and Deployment
9. Advanced PyTorch Features
10. PyTorch Ecosystem Integration
Neural Network Construction
The nn.Module Framework
Module Architecture
Base Class Structure
Inheritance Patterns
Module Lifecycle
Initialization Phase
Forward Pass Execution
Parameter Registration
Parameter Management
Automatic Parameter Detection
Parameter Initialization
Parameter Access Methods
Submodule Organization
Nested Module Structure
Module Containers
Module States
Training vs Evaluation Mode
State Persistence
Core Layer Types
Linear Layers
nn.Linear
Weight and Bias Parameters
Input-Output Dimensions
Initialization Strategies
Convolutional Layers
1D Convolutions
nn.Conv1d
Temporal Convolutions
2D Convolutions
nn.Conv2d
Image Processing Applications
3D Convolutions
nn.Conv3d
Volumetric Data Processing
Convolution Parameters
Kernel Size and Stride
Padding and Dilation
Groups and Depth-wise Convolution
Pooling Layers
Max Pooling
nn.MaxPool1d
nn.MaxPool2d
nn.MaxPool3d
Average Pooling
nn.AvgPool1d
nn.AvgPool2d
nn.AvgPool3d
Adaptive Pooling
nn.AdaptiveMaxPool2d
nn.AdaptiveAvgPool2d
Output Size Control
Recurrent Layers
Basic RNN
nn.RNN
Vanilla RNN Cells
LSTM Networks
nn.LSTM
Long Short-Term Memory
GRU Networks
nn.GRU
Gated Recurrent Units
Recurrent Layer Configuration
Bidirectional Processing
Multi-layer Stacking
Dropout in RNNs
Normalization Layers
Batch Normalization
nn.BatchNorm1d
nn.BatchNorm2d
nn.BatchNorm3d
Layer Normalization
nn.LayerNorm
Transformer Applications
Group Normalization
nn.GroupNorm
Small Batch Scenarios
Instance Normalization
nn.InstanceNorm2d
Style Transfer Applications
Regularization Layers
Dropout
nn.Dropout
Random Neuron Deactivation
Spatial Dropout
nn.Dropout2d
nn.Dropout3d
Channel-wise Dropout
Activation Functions
ReLU Family
nn.ReLU
nn.LeakyReLU
nn.PReLU
nn.ELU
nn.SELU
nn.GELU
Sigmoid and Tanh
nn.Sigmoid
nn.Tanh
Saturation Properties
Softmax Functions
nn.Softmax
nn.LogSoftmax
Dimension Specification
Advanced Activations
nn.Swish
nn.Mish
Custom Activation Functions
Loss Functions
Regression Losses
nn.MSELoss
nn.L1Loss
nn.SmoothL1Loss
nn.HuberLoss
Classification Losses
nn.CrossEntropyLoss
nn.NLLLoss
nn.BCELoss
nn.BCEWithLogitsLoss
Ranking and Margin Losses
nn.MarginRankingLoss
nn.HingeEmbeddingLoss
nn.TripletMarginLoss
Custom Loss Functions
Defining Custom Losses
Combining Multiple Losses
Module Containers
Sequential Container
nn.Sequential
Linear Layer Stacking
Ordered Execution
ModuleList Container
nn.ModuleList
Dynamic Layer Management
Iteration and Indexing
ModuleDict Container
nn.ModuleDict
Named Layer Access
Dictionary-like Interface
Custom Containers
Building Complex Architectures
Conditional Execution Paths
Custom Module Development
Defining Custom Modules
Class Structure
init Method Implementation
forward Method Definition
Parameter Registration
nn.Parameter Usage
Buffer Registration
Module Composition
Combining Existing Modules
Hierarchical Design
Advanced Module Features
Hooks and Callbacks
State Management
Serialization Support
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6. Data Handling and Processing