Pytorch Deep Learning

• Introduction to PyTorch: Explore the fundamentals, advantages, and applications of PyTorch in industry and academia.
• Environment Setup: Instructions on installing and setting up PyTorch on various platforms (Linux, Windows, Mac).
• PyTorch Tensors: In-depth exploration of tensors, the core component of PyTorch, including creation, manipulation, and operations.

This revised syllabus provides a structured and detailed pathway through learning PyTorch, ensuring that learners not only gain theoretical knowledge but also practical skills in building, optimizing, and deploying neural networks using PyTorch.

• Tensor Operations: Detailed guide on tensor operations including addition, subtraction, multiplication, division, and more complex mathematical operations.
• Matrix Operations: Focus on matrix multiplication, understanding its role in neural networks, and troubleshooting common tensor shape errors.
• Advanced Tensor Manipulation: Techniques for reshaping, squeezing, unsqueezing, and indexing tensors to prepare data for models.

• Linear Regression Models: Constructing linear regression models from scratch, understanding the underlying mathematics and implementation in PyTorch.
• Classification Models: Building and training neural networks for classification tasks, understanding network architectures, and performance metrics.
• Computer Vision with CNNs: Application of convolutional neural networks to image data for classification, detection, and segmentation tasks.

• Custom Datasets and DataLoaders: Handling custom datasets, transforming them into tensors, and utilizing PyTorch’s DataLoader for efficient model training.
• Transfer Learning: Leveraging pre-trained models to boost performance and efficiency on new tasks with a focus on enhancing FoodVision Mini.
• Model Optimization: Exploring techniques like quantization and pruning to enhance model performance and efficiency.

• Experiment Tracking with TensorBoard: Implementing TensorBoard to track experiments, visualize model performance, and select the best models.
• Deployment Strategies: Techniques and tools for deploying PyTorch models into production, including Docker, Kubernetes, and cloud platforms.

•  Replicating Machine Learning Research: Practical experience in replicating results from cutting-edge research papers, focusing on architectures like Vision Transformers.
• Model Debugging and Evaluation: Strategies for debugging PyTorch models, understanding outputs, and employing various evaluation metrics.

• Capstone Project: A comprehensive project that synthesizes all the learned skills to design, build, train, and deploy a model following MLOps principles.
• Real-World Application Scenarios*: Applying learned models to solve real-world problems across different domains such as healthcare, autonomous driving, or financial forecasting.

• Scaling PyTorch Applications: Techniques for scaling PyTorch applications horizontally and vertically to handle larger datasets and more complex models.
• Advanced Deployment Techniques: Deep dive into advanced model deployment strategies including continuous integration and delivery pipelines for machine learning models.

• Review and Final Assessment: Comprehensive review of all topics covered with a final assessment to test knowledge and skills.
• Future Learning Paths: Guidance on continuing education and research opportunities in machine learning and deep learning post-course.