Deep Learning with PyTorch

Deep Learning with PyTorch involves the practical application of deep learning principles using the open-source PyTorch framework. Celebrated for its Python-first design, flexibility offered by dynamic computational graphs, and robust GPU-accelerated tensor computations, PyTorch provides an intuitive yet powerful platform for researchers and developers. It streamlines the entire process of building, training, and deploying complex neural network architectures, from initial prototyping and experimentation to production-level implementation, making it a leading choice for a wide range of artificial intelligence tasks.

1. Introduction to Deep Learning and PyTorch
   1. Overview of Artificial Intelligence
      1. Definition and Scope of AI
      2. Historical Milestones in AI
      3. AI Applications in Modern Technology
   2. Machine Learning Fundamentals
      1. Definition of Machine Learning
      2. Types of Machine Learning
         1. Supervised Learning
         2. Unsupervised Learning
         3. Reinforcement Learning
         4. Semi-supervised Learning
      3. Machine Learning Workflow
         1. Data Collection and Preparation
         2. Model Selection and Training
         3. Evaluation and Validation
         4. Deployment and Monitoring
      4. Relationship Between AI, Machine Learning, and Deep Learning
   3. Deep Learning Concepts
      1. Definition of Deep Learning
      2. Key Characteristics of Deep Learning
      3. Advantages of Deep Learning
      4. Limitations of Deep Learning
      5. Applications of Deep Learning
         1. Computer Vision
         2. Natural Language Processing
         3. Speech Recognition
         4. Recommendation Systems
   4. Biological Inspiration
      1. Neurons and Synapses
      2. Neural Signal Transmission
      3. Artificial Neurons
      4. Mathematical Modeling of Neurons
   5. Neural Network Architecture
      1. Layers in Neural Networks
         1. Input Layer
         2. Hidden Layers
         3. Output Layer
      2. Network Topology
         1. Feedforward Networks
         2. Recurrent Networks
         3. Convolutional Networks
      3. Network Depth and Width
   6. The Learning Process
      1. Forward Propagation
      2. Backpropagation Algorithm
      3. Gradient Descent
      4. Training Phase
      5. Validation Phase
      6. Testing Phase
      7. Data Splitting Strategies
   7. Introduction to PyTorch
      1. History and Development of PyTorch
      2. PyTorch Philosophy and Design Principles
      3. Key Features of PyTorch
         1. Dynamic Computation Graphs
         2. Pythonic API
         3. GPU Acceleration
         4. Research-Friendly Design
      4. Comparison with Other Frameworks
         1. PyTorch vs TensorFlow
         2. PyTorch vs Keras
         3. PyTorch vs JAX
      5. The PyTorch Ecosystem
         1. TorchVision for Computer Vision
         2. TorchText for Natural Language Processing
         3. TorchAudio for Audio Processing
         4. PyTorch Lightning
         5. Hugging Face Transformers
   8. Setting Up the Development Environment
      1. System Requirements
         1. Hardware Requirements
         2. Operating System Compatibility
      2. Installing PyTorch
         1. Installation via pip
         2. Installation via conda
         3. Installing from Source
      3. CPU vs GPU Installation
         1. CUDA Compatibility
         2. Installing CUDA Toolkit
         3. ROCm for AMD GPUs
      4. Verifying Installation
         1. Checking PyTorch Version
         2. Testing GPU Availability
         3. Running Basic Operations
      5. Essential Python Libraries
         1. NumPy for Numerical Computing
         2. Matplotlib for Visualization
         3. Scikit-learn for Machine Learning Utilities
         4. Pandas for Data Manipulation
         5. Jupyter for Interactive Development
      6. Development Environment Setup
         1. Jupyter Notebooks
         2. IDEs and Code Editors
         3. Virtual Environments