PyTorch Library

PyTorch is an open-source machine learning library that has become a cornerstone of modern Deep Learning. Developed by Meta AI and based on the Torch library, its fundamental data structure is the tensor, a multi-dimensional array optimized for high-performance computation on GPUs. PyTorch is distinguished by its use of dynamic computational graphs and an imperative, Pythonic interface, which offers developers flexibility and ease of debugging during model development. The library's powerful `autograd` module automates the calculation of gradients, a critical process for training neural networks via backpropagation, making it a favored tool for both rapid research prototyping and robust production deployment.

  1. Introduction to PyTorch
    1. What is PyTorch
      1. Definition and Purpose
        1. History and Development
          1. Open Source Nature
          2. Core Philosophy and Design Principles
            1. Imperative Execution Model
              1. Dynamic Computational Graphs
                1. Pythonic Interface Design
                  1. Research-First Approach
                  2. Key Features and Capabilities
                    1. Automatic Differentiation
                      1. GPU Acceleration Support
                        1. Neural Network Building Blocks
                          1. Flexible Architecture
                            1. NumPy Interoperability
                            2. PyTorch in the Deep Learning Landscape
                              1. Comparison with TensorFlow
                                1. Comparison with JAX
                                  1. Use Cases and Applications
                                    1. Industry Adoption
                                    2. Installation and Setup
                                      1. System Requirements
                                        1. Operating System Support
                                          1. Python Version Compatibility
                                            1. Hardware Requirements
                                            2. Installation Methods
                                              1. Installation via pip
                                                1. Installation via conda
                                                  1. Installing from Source
                                                  2. GPU Support Setup
                                                    1. CUDA Installation
                                                      1. ROCm for AMD GPUs
                                                        1. Verifying GPU Availability
                                                        2. Environment Verification
                                                          1. Import Testing
                                                            1. Version Checking
                                                              1. Basic Functionality Testing
                                                              2. Common Installation Issues
                                                                1. Troubleshooting Guide
                                                                  1. Version Conflicts
                                                                    1. Path Configuration

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                                                                2. Tensors: The Foundation