MIT HAN Lab Courses

Efficient Fine-tuning and Prompt Engineering - Lecture 20

Explore efficient fine-tuning techniques and prompt engineering strategies for optimizing large language models in machine learning applications.

• YouTube
• 1 hour 18 minutes
• Self-Paced
• Free Video

Efficient Fine-tuning and Prompt Engineering - Lecture 20

Explore efficient fine-tuning techniques and prompt engineering strategies for optimizing large language models in machine learning applications.

• YouTube
• 1 hour 17 minutes
• Self-Paced
• Free Video

On-Device Training and Transfer Learning - Lecture 19

Explore on-device training and transfer learning techniques for efficient machine learning, focusing on practical applications and cutting-edge methodologies.

• YouTube
• 1 hour 17 minutes
• Self-Paced
• Free Video

On-Device Training and Transfer Learning - Lecture 19

Explore on-device training and transfer learning techniques for efficient machine learning, focusing on practical applications and cutting-edge methodologies.

• YouTube
• 1 hour 17 minutes
• Self-Paced
• Free Video

Distributed Training for Efficient Machine Learning - Part II - Lecture 18

Explore advanced distributed training techniques for machine learning, focusing on efficiency and scalability in large-scale systems.

• YouTube
• 55 minutes
• Self-Paced
• Free Video

Distributed Training: Techniques and Strategies - Part II - Lecture 18

Explore advanced distributed training techniques for machine learning, focusing on efficiency and scalability in large-scale AI systems.

• YouTube
• 55 minutes
• Self-Paced
• Free Video

Distributed Training - Part I - Lecture 17

Explore distributed training techniques for efficient machine learning, covering key concepts and strategies for scaling ML models across multiple devices.

• YouTube
• 1 hour 1 minute
• Self-Paced
• Free Video

Distributed Training for Efficient Machine Learning - Part I - Lecture 17

Explore distributed training techniques for machine learning, focusing on data and model parallelism strategies to enhance efficiency and scalability.

• YouTube
• 1 hour 1 minute
• Self-Paced
• Free Video

Diffusion Models in Efficient Machine Learning - Lecture 16

Explore diffusion models in-depth, covering theory, applications, and efficient implementation techniques for advanced machine learning practitioners.

• YouTube
• 1 hour 16 minutes
• Self-Paced
• Free Video

Diffusion Models in Machine Learning - Lecture 16

Explore diffusion models in machine learning, covering theory, applications, and efficient implementation techniques for advanced AI image generation.

• YouTube
• 1 hour 16 minutes
• Self-Paced
• Free Video

EfficientML.ai: GAN, Video, and Point Cloud - Lecture 15

Explore advanced machine learning concepts: GANs, video processing, and point cloud analysis. Gain insights into cutting-edge techniques for efficient AI applications.

• YouTube
• 1 hour 8 minutes
• Self-Paced
• Free Video

EfficientML.ai: GAN, Video, and Point Cloud - Lecture 15

Explore advanced machine learning topics: GANs, video processing, and point cloud analysis. Gain insights into cutting-edge AI applications and techniques.

• YouTube
• 1 hour 8 minutes
• Self-Paced
• Free Video

Vision Transformer - Lecture 14

Explore Vision Transformers in deep learning, covering architecture, training, and applications. Gain insights into this powerful model for computer vision tasks.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

Vision Transformer - Lecture 14

Explore Vision Transformers in deep learning, covering architecture, training, and applications. Gain insights into this powerful model for computer vision tasks.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

AWQ for LLM Quantization - Efficient Inference Framework for Large Language Models

Explore Activation-aware Weight Quantization (AWQ) for efficient large language model deployment, reducing memory requirements while preserving performance across domains and modalities.

• YouTube
• 21 minutes
• Self-Paced
• Free Video