Learn to build custom neural-network layers and accelerate model training with performance-driven PyTorch techniques. This hands-on, engineer-focused course teaches you how to design differentiable modules, diagnose bottlenecks, and apply optimizations like mixed precision and gradient accumulation to significantly boost training throughput.

This course teaches you how to fine-tune powerful vision models and optimize their training for real-world performance. You’ll start by applying transfer learning with a pre-trained ViT-B/16 model, learning how to freeze and selectively unfreeze layers to adapt general visual representations to domain-specific datasets such as retail product images. You’ll then analyze and compare learning-rate schedules, including cosine decay and the one-cycle policy, to understand how each strategy shapes training stability, convergence speed, and validation accuracy. Through hands-on labs, experiment logging, and training-curve interpretation, you’ll practice making informed decisions about which layers to update, which LR schedule to select, and how to balance accuracy with training efficiency. By the end of the course, you’ll be able to fine-tune transformer-based models effectively and choose learning-rate strategies that reduce training time without sacrificing performance.

In this short, hands-on course, you’ll learn how to build fast, efficient AI training and inference pipelines by optimizing both data loading and computational graphs. You’ll start by creating parallel, high-throughput data pipelines that keep GPUs consistently busy and reduce training bottlenecks. Then you’ll analyze a model’s computational graph to identify and remove redundant operations that slow execution. Through focused lesson videos, practical labs, and guided coach activities, you’ll re-export a streamlined model and validate real latency improvements. By the end, you’ll be able to diagnose performance issues, streamline pipelines, and apply optimization techniques that make AI systems faster, more reliable, and more cost-efficient.

This course teaches you how to evaluate machine learning experiments visually and how to transform prototype scripts into reusable, maintainable workflows.

You’ll start by exploring how to use visual dashboards like TensorBoard to compare model variants using metrics such as accuracy curves, loss trajectories, and compute usage. Then, you’ll learn how to refactor model training code into standardized structures using tools like LightningModules and DataModules. Through short videos, readings, hands-on Learnings and a final assessment, you’ll gain confidence in comparing models, understanding experiment performance, and creating workflows that your entire team can use. Whether you're presenting model trade-offs or preparing code for a shared repository, you’ll walk away ready to support real-world ML development with clarity and rigor.