MIT HAN Lab Courses

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

PockEngine: Sparse and Efficient Fine-tuning in a Pocket

Explore sparse and efficient fine-tuning techniques for AI models with PockEngine, enhancing performance while reducing computational demands.

• YouTube
• 22 minutes
• 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

Transformer and Large Language Models - Part II - Lecture 13

Explore advanced concepts of Transformers and Large Language Models, focusing on efficiency and optimization techniques in AI and machine learning.

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

Transformer and Large Language Models - Part II - Lecture 13

Explore advanced concepts of Transformers and Large Language Models in this in-depth lecture from MIT's EfficientML course.

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

Transformer and Large Language Models - Part I - Lecture 12

Explore Transformer architecture and Large Language Models in this comprehensive MIT lecture, covering key concepts and recent advancements in AI.

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

Transformer and Large Language Models - Part I - Lecture 12

Explore Transformer architecture and Large Language Models in this comprehensive MIT lecture, covering key concepts and recent advancements in AI.

• YouTube
• 1 hour 20 minutes
• Self-Paced
• Free Video

TinyEngine and Parallel Processing - Lecture 11

Explore TinyEngine and parallel processing techniques for efficient machine learning on resource-constrained devices, enhancing performance and reducing energy consumption.

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