Post-Training Quantization Courses and Certifications

MIT HAN Lab

Quantization in Neural Networks - Part II - Lecture 6

Explore advanced neural network quantization techniques, including post-training quantization, quantization-aware training, binary/ternary quantization, and mixed-precision quantization.

• YouTube
• 1 hour 11 minutes
• On-Demand
• Free Video

EDGE AI FOUNDATION

TinyDenoiser: RNN-based Speech Enhancement on Multi-Core MCU with Mixed FP16-INT8 Post-Training Quantization

Explore advanced speech enhancement techniques using RNN-based algorithms optimized for microcontrollers, featuring mixed-precision quantization and efficient parallel processing for improved performance.

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

EDGE AI FOUNDATION

TinyDenoiser: RNN-based Speech Enhancement on Multi-Core MCU with Mixed FP16-INT8 Post-Training Quantization

Discover how to optimize speech enhancement algorithms using RNN-based models on multi-core MCUs, focusing on mixed-precision quantization and efficient software pipelining techniques.

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

MIT HAN Lab

Quantization for Efficient Machine Learning - Part I - Lecture 5

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

• YouTube
• 1 hour 15 minutes
• On-Demand
• Free Video

MIT HAN Lab

Quantization for Efficient Machine Learning - Part II

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

• YouTube
• 1 hour 11 minutes
• On-Demand
• Free Video

MIT HAN Lab

EfficientML.ai: Quantization Part II - Lecture 6

Explore advanced quantization techniques for efficient machine learning, including Rooney quantization, scaling factors, and fine-tuning strategies.

• YouTube
• 1 hour 15 minutes
• On-Demand
• Free Video

MIT HAN Lab

EfficientML.ai - Quantization Part I - Lecture 5

Explore quantization techniques for efficient machine learning, focusing on part I of the topic in this MIT lecture by Prof. Song Han.

• YouTube
• 1 hour 15 minutes
• On-Demand
• Free Video

AI Bites

Quantization in Deep Learning: Types, Algorithms, and Implementation

Explore quantization techniques in deep learning, from uniform to non-uniform approaches, and learn practical implementation strategies for optimizing large-scale neural networks.

• YouTube
• 13 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

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
• On-Demand
• Free Video

MIT HAN Lab

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
• On-Demand
• Free Video

MIT HAN Lab

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
• On-Demand
• Free Video

MLOps World: Machine Learning in Production

Scaling of Quantized Large Language Models for Efficient Inference

Discover how quantization affects large language model scaling and develop empirical laws for efficient LLM inference deployment through theoretical insights and practical compression techniques.

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