Model Compression Courses and Certifications

Advanced Model Compression Techniques

Master advanced model compression techniques to reduce ML model size while maintaining performance through quantization, pruning, and collaborative pipelines for mobile deployment.

• Udacity
• 11 hours
• On-Demand
• Paid Course

Neural Magic

Applying Second-Order Pruning Algorithms for SOTA Model Compression

Explore second-order pruning algorithms for model compression, achieving higher sparsity while maintaining accuracy. Learn to apply these techniques to your ML projects for improved efficiency.

• YouTube
• 42 minutes
• On-Demand
• Free Video

MIT HAN Lab

Pruning and Sparsity in Machine Learning - Part I (EfficientML.ai Lecture 3)

Explore pruning and sparsity techniques for efficient machine learning, focusing on reducing model size and computational requirements while maintaining performance.

• YouTube
• 1 hour 9 minutes
• On-Demand
• Free Video

Discover AI

LLM Quantization Tutorial: QLoRA, GPTQ, and LLama.cpp Implementation for LLama 2

Master LLM quantization techniques and implement efficient model compression using QLoRA, GPTQ, and Llamacpp, with practical guidance for running optimized LLMs locally across different platforms.

• YouTube
• 27 minutes
• On-Demand
• Free Video

Shaw Talebi

Compressing Large Language Models (LLMs) with Python Code - 3 Techniques

Explore three LLM compression techniques: quantization, pruning, and knowledge distillation. Learn to implement these methods with Python code examples for efficient model optimization.

• YouTube
• 24 minutes
• On-Demand
• Free Video

MIT HAN Lab

Knowledge Distillation in Efficient Machine Learning - Lecture 9

Explore knowledge distillation techniques for efficient machine learning, covering theory and practical applications in model compression and performance enhancement.

• YouTube
• 1 hour
• On-Demand
• Free Video

MIT HAN Lab

Knowledge Distillation in Efficient Machine Learning - Lecture 9

Explore knowledge distillation techniques for efficient machine learning, covering theory and practical applications in model compression and performance enhancement.

• YouTube
• 1 hour
• On-Demand
• Free Video

MIT HAN Lab

Pruning and Sparsity in Machine Learning - Part II - Lecture 4

Explore advanced pruning techniques and sparsity concepts for efficient machine learning, focusing on practical applications and cutting-edge research in neural network optimization.

• YouTube
• 1 hour 18 minutes
• On-Demand
• Free Video

MIT HAN Lab

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Explore activation-aware weight quantization for compressing and accelerating large language models. Learn about AWQ's innovative approach to LLM optimization.

• YouTube
• 19 minutes
• On-Demand
• Free Video

Unify

MiniLLM: Knowledge Distillation of Large Language Models

Explore knowledge distillation techniques for large language models, focusing on reverse KLD to improve student model precision and response quality.

• YouTube
• 52 minutes
• On-Demand
• Free Video

EDGE AI FOUNDATION

From the Lab to the Edge: Post-Training Compression for Deep Neural Networks

Discover how to efficiently deploy deep neural networks on edge devices using post-training compression techniques, achieving high speeds with minimal accuracy loss through quantization and pruning methods.

• YouTube
• 58 minutes
• On-Demand
• Free Video

MIT HAN Lab

Knowledge Distillation in Efficient Machine Learning - Lecture 9

Explore knowledge distillation techniques for efficient machine learning, covering theory, applications, and practical implementation strategies.

• YouTube
• 59 minutes
• On-Demand
• Free Video

MIT HAN Lab

Knowledge Distillation in Efficient Machine Learning - Lecture 9

Explore knowledge distillation techniques for efficient machine learning, covering theory, applications, and practical implementation strategies.

• YouTube
• 58 minutes
• On-Demand
• Free Video

MIT HAN Lab

Pruning and Sparsity in Machine Learning - Part II - Lecture 4

Explore advanced pruning techniques and sparsity concepts for efficient machine learning, focusing on practical applications and cutting-edge research.

• YouTube
• 1 hour 9 minutes
• On-Demand
• Free Video

MIT HAN Lab

AI Model Efficiency Toolkit (AIMET) - Lecture 25

Explore AI model efficiency techniques with Qualcomm AI Research. Learn about AIMET, community research, core technologies, and industry applications for optimizing deep learning models.

• YouTube
• 45 minutes
• On-Demand
• Free Video