Deep Learning with PyTorch

  1. Practical Considerations and Best Practices
    1. Hardware Management
      1. Device Detection
        1. CUDA Availability
          1. Device Enumeration
            1. GPU Properties
            2. Device Management
              1. Moving Tensors to Device
                1. Moving Models to Device
                  1. Device Context Management
                  2. Memory Management
                    1. GPU Memory Monitoring
                      1. Memory Cleanup
                        1. Out-of-Memory Handling
                        2. Multi-GPU Training
                          1. Data Parallel Training
                            1. Distributed Data Parallel
                              1. Model Parallel Training
                            2. Reproducibility
                              1. Random Seed Management
                                1. PyTorch Random Seeds
                                  1. NumPy Random Seeds
                                    1. Python Random Seeds
                                    2. Deterministic Operations
                                      1. CUDA Deterministic Mode
                                        1. CuDNN Deterministic
                                        2. Environment Reproducibility
                                          1. Dependency Management
                                            1. Version Pinning
                                          2. Debugging and Profiling
                                            1. Common Error Patterns
                                              1. Shape Mismatches
                                                1. Device Mismatches
                                                  1. Gradient Flow Issues
                                                  2. Debugging Tools
                                                    1. Assertion Checks
                                                      1. Gradient Checking
                                                      2. Performance Profiling
                                                        1. PyTorch Profiler
                                                          1. Memory Profiling
                                                            1. Bottleneck Identification
                                                          2. Monitoring and Visualization
                                                            1. Training Monitoring
                                                              1. Loss Tracking
                                                                1. Metric Logging
                                                                  1. Learning Rate Monitoring
                                                                  2. TensorBoard Integration
                                                                    1. Scalar Logging
                                                                      1. Image Logging
                                                                        1. Graph Visualization
                                                                          1. Hyperparameter Tracking
                                                                          2. Model Visualization
                                                                            1. Architecture Visualization
                                                                              1. Feature Map Visualization
                                                                                1. Attention Visualization
                                                                              2. Code Organization
                                                                                1. Project Structure
                                                                                  1. Directory Organization
                                                                                    1. Configuration Management
                                                                                      1. Modular Design
                                                                                      2. Code Quality
                                                                                        1. Documentation Standards
                                                                                          1. Type Hints
                                                                                            1. Error Handling
                                                                                            2. Experiment Management
                                                                                              1. Hyperparameter Tracking
                                                                                                1. Experiment Logging
                                                                                                  1. Result Comparison
                                                                                                2. Performance Optimization
                                                                                                  1. Training Acceleration
                                                                                                    1. Mixed Precision Training
                                                                                                      1. Gradient Accumulation
                                                                                                        1. Learning Rate Scaling
                                                                                                        2. Data Loading Optimization
                                                                                                          1. Parallel Data Loading
                                                                                                            1. Data Prefetching
                                                                                                              1. Memory Pinning
                                                                                                              2. Model Architecture Optimization
                                                                                                                1. Efficient Layer Design
                                                                                                                  1. Computational Graph Optimization

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                                                                                                              9. Model Deployment and Production

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                                                                                                              1. Introduction to Deep Learning and PyTorch