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Model Compression Courses and Certifications

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Model Compression Courses and Certifications

How Well Do Sparse Models Transfer? - Exploring Transfer Performance in Computer Vision and NLP

Applying Second-Order Pruning Algorithms for SOTA Model Compression

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

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

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

AI Model Efficiency Toolkit (AIMET) - Lecture 25

AI Model Efficiency Toolkit (AIMET) - Lecture 25

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

The Era of 1-bit LLMs Explained - BitNet b1.58 and New Scaling Laws

SliceGPT Explained - Compressing Large Language Models

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

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

Memory Optimization Techniques for On-Device Large Language Models

LLM Post-Training Techniques - Lecture 14

거대 인공지능 모델 시대에 스타트업의 차별화 방향 - AI 모델 경량화 기술을 중심으로

Model Compression Courses and Certifications

How Well Do Sparse Models Transfer? - Exploring Transfer Performance in Computer Vision and NLP

Applying Second-Order Pruning Algorithms for SOTA Model Compression

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

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

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

AI Model Efficiency Toolkit (AIMET) - Lecture 25

AI Model Efficiency Toolkit (AIMET) - Lecture 25

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

The Era of 1-bit LLMs Explained - BitNet b1.58 and New Scaling Laws

SliceGPT Explained - Compressing Large Language Models

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

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

Memory Optimization Techniques for On-Device Large Language Models

LLM Post-Training Techniques - Lecture 14

거대 인공지능 모델 시대에 스타트업의 차별화 방향 - AI 모델 경량화 기술을 중심으로

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