Overview

Master advanced PyTorch techniques to build high-performing, efficient deep learning models. In this course, you’ll expand your skills in hyperparameter optimization, model profiling, and workflow efficiency. You’ll experiment with learning rate schedulers, tackle overfitting, and use automated hyperparameter tuning with Optuna to boost model performance. Learn how to design flexible architectures, measure model efficiency with the PyTorch Profiler, and make the most of your compute resources. You’ll also dive into real-world applications using TorchVision for computer vision tasks like loading, transforming, and augmenting image data, and leveraging Hugging Face for natural language processing. You’ll apply transfer learning and fine-tune pre-trained models to adapt them for new problems. By the end, you’ll know how to train smarter, optimize deeper, and build PyTorch models ready for production-level deployment.

Syllabus

Hyperparameter Optimization

This module focuses on optimizing machine learning models through systematic evaluation and hyperparameter tuning techniques. Students will learn to assess model performance using key evaluation metrics like accuracy, precision, recall, and F1-score, then apply various optimization strategies to improve their models. The course covers practical techniques including learning rate scheduling, flexible architecture design, and automated hyperparameter tuning using tools like Optuna. By the end of this module, learners will understand how to balance model performance with efficiency considerations like inference time and memory usage to select optimal models for real-world applications.

Working with Images using TorchVision

This module provides a comprehensive introduction to TorchVision, PyTorch's computer vision library that offers essential tools for image processing, data handling, and model deployment. Students will explore TorchVision's core components including image transforms, preprocessing pipelines, built-in datasets, and pretrained models. The course emphasizes practical applications through hands-on experience with data augmentation techniques, transfer learning, and fine-tuning strategies. By the end of this module, learners will be equipped to leverage TorchVision's powerful utilities for real-world computer vision projects and understand how to adapt pretrained models for custom tasks.

Working with Text using Hugging Face

This module introduces Natural Language Processing (NLP) fundamentals using PyTorch, covering the essential pipeline from raw text to trained models. Students will learn how to transform text data into numerical representations through tokenization, tensorization, and embedding techniques, while exploring both traditional methods and modern approaches using pretrained models. The course emphasizes practical implementation skills including building custom tokenizers, working with HuggingFace transformers, and creating text classification models. By the end of this module, learners will understand how to leverage both static and dynamic embeddings, and apply transfer learning techniques to fine-tune state-of-the-art NLP models for various text processing tasks.

Efficient Training Pipelines

This module focuses on optimizing machine learning workflows through efficient data handling and training techniques in PyTorch. Students will learn to identify and eliminate performance bottlenecks that can slow down model training, particularly around data loading and GPU utilization. The course covers advanced DataLoader configurations, profiling tools, and modern optimization strategies like mixed precision training and gradient accumulation. By the end of this module, learners will understand how to create high-performance training pipelines using PyTorch Lightning and other optimization tools to maximize computational efficiency.