MIT HAN Lab Courses

Distributed Training Methods for Efficient Machine Learning - Part 1

Explore distributed training fundamentals, from data parallelism to advanced techniques like ZeRO and pipeline parallelism, enhancing your ML model deployment capabilities.

• YouTube
• 1 hour 10 minutes
• Self-Paced
• Free Video

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
• Self-Paced
• Free Video

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
• Self-Paced
• Free Video

Neural Architecture Search Part II - Lecture 8

Explore advanced neural architecture search techniques, including evolutionary algorithms and reinforcement learning for optimizing deep learning models.

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

Neural Architecture Search Part II - Lecture 8

Explore advanced neural architecture search techniques, including evolutionary algorithms and reinforcement learning, to optimize deep learning models for efficiency and performance.

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

Neural Architecture Search Part I - Lecture 7

Explore neural architecture search techniques for optimizing deep learning models, focusing on automated design and efficiency in AI systems.

• YouTube
• 1 hour 6 minutes
• Self-Paced
• Free Video

EfficientML.ai: Quantization Part II - Lecture 6

Explore advanced quantization techniques for efficient machine learning, covering post-training and quantization-aware training methods to optimize model performance.

• YouTube
• 1 hour 9 minutes
• Self-Paced
• Free Video

Neural Architecture Search Part I - Lecture 7

Explore neural architecture search techniques for optimizing deep learning models, covering fundamental concepts and cutting-edge approaches in AI efficiency.

• YouTube
• 1 hour 6 minutes
• Self-Paced
• Free Video

EfficientML.ai - Quantization Part II - Lecture 6

Explore advanced quantization techniques for efficient machine learning, including post-training and quantization-aware training methods.

• YouTube
• 1 hour 9 minutes
• Self-Paced
• Free Video

EfficientML.ai - Quantization Part I - Lecture 5

Explore quantization techniques for efficient machine learning, covering fundamental concepts and practical applications in neural network optimization.

• YouTube
• 1 hour 7 minutes
• Self-Paced
• Free Video

EfficientML.ai - Quantization Part I - Lecture 5

Explore quantization techniques for efficient machine learning, covering fundamental concepts and practical applications in neural network optimization.

• YouTube
• 1 hour 7 minutes
• Self-Paced
• Free Video

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
• Self-Paced
• Free Video

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 9 minutes
• Self-Paced
• Free Video

Pruning and Sparsity in Machine Learning - Part I

Explore pruning and sparsity techniques in machine learning, focusing on improving model efficiency without sacrificing performance. Learn key concepts and practical applications.

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

Basics of Neural Networks - EfficientML.ai Lecture 2

Explore neural network fundamentals, including architecture, training, and optimization techniques, in this comprehensive MIT lecture on efficient machine learning.

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