Advanced
Advanced Optimization and Research Topics
Explore cutting-edge optimization techniques, efficient architectures, and emerging research directions. Stay at the frontier of deep learning.
Introduction
Explore cutting-edge optimization techniques, efficient architectures, and emerging research directions. Stay at the frontier of deep learning.
4 Lessons
25h Est. Time
4 Objectives
1 Assessment
By completing this module you will be able to:
✓ Master advanced optimizers beyond Adam (AdamW, Lion, etc.)
✓ Understand model compression and quantization techniques
✓ Learn about efficient attention mechanisms and sparse models
✓ Explore recent research papers and emerging techniques
Lessons
Work through each lesson in order. Each one builds on the concepts from the previous lesson.
1
Distributed Training and Scaling
2
Fast Inference and Optimization
3
Model Compression and Quantization
4
MLOps and Model Management
Recommended Reading
Supplement your learning with these selected chapters from the course library.
Mastering PyTorch 2e
Chapters 13-15
Machine Learning with PyTorch and Scikit-Learn
Chapters 13-15
Module Assessment
Advanced Optimization and Research Topics
Question 1 of 3