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#Model Compression

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Association for Computing Machinery (ACM)

GUITAR: Gradient Pruning toward Fast Neural Ranking - Efficiency for Search

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Explore gradient pruning techniques for optimizing neural ranking models, enhancing search efficiency and performance.
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1 Lesons
11 minutes
On-Demand
Free-Video
MLOps World: Machine Learning in Production

Leaner, Greener and Faster PyTorch Inference with Quantization

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Explore PyTorch quantization techniques to optimize neural networks, reducing size and improving speed without sacrificing accuracy. Learn implementation strategies and real-world applications.
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1 Lesons
1 hour 38 minutes
On-Demand
Free-Video
MLOps World: Machine Learning in Production

A Gentle Introduction to Sparsity with a Concrete Example

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Explore sparsification techniques for ML models, including popular methods and a concrete example to understand their benefits.
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1 Lesons
26 minutes
On-Demand
Free-Video
MIT HAN Lab

MCUNet and TinyML - Lecture 10

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Explore MCUNet and TinyML for efficient machine learning on microcontrollers, covering design principles and real-world applications in resource-constrained environments.
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1 Lesons
1 hour 7 minutes
On-Demand
Free-Video
MIT HAN Lab

MCUNet and TinyML - Lecture 10

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Explore MCUNet and TinyML for efficient machine learning on microcontrollers. Learn optimization techniques and real-world applications in resource-constrained environments.
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1 Lesons
1 hour 7 minutes
On-Demand
Free-Video
Association for Computing Machinery (ACM)

Self-Improving Teacher Cultivates Better Student: Distillation Calibration for Multimodal Large Language Models - Lecture 3.3

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Explore innovative distillation calibration techniques for multimodal large language models, focusing on self-improving teacher methods to enhance student model performance.
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1 Lesons
14 minutes
On-Demand
Free-Video
Anyscale

Optimizing vLLM Performance Through Quantization - Model Compression Techniques

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Discover practical techniques for model quantization in vLLM deployments, exploring FP8, INT8, and INT4 methods to enhance performance and reduce memory usage while maintaining efficiency.
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1 Lesons
38 minutes
On-Demand
Free-Video
ADC - Audio Developer Conference

Leveraging Pruning and Quantization for Efficient Real-Time Audio Applications

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Explore pruning and quantization techniques to enhance efficiency of complex audio models for real-time processing in resource-constrained environments. Gain insights into model compression for audio applications.
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1 Lesons
31 minutes
On-Demand
Free-Video
The Machine Learning Engineer

LLMOps: Quantization Models and Inference with ONNX Generative Runtime

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Explore ONNX runtime GPU support for inference with generative models, including Phi3-mini quantization and conversion techniques.
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1 Lesons
30 minutes
On-Demand
Free-Video
Data Science Conference

Lightweight Deep Learning on Edge Devices - Energy Efficient Approaches

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Discover efficient deep learning techniques for edge devices, focusing on energy-saving approximation methods that maintain accuracy while improving performance on smartphones and IoT devices.
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13 Lesons
32 minutes
On-Demand
Free-Video
MIT HAN Lab

LLM Deployment Techniques - Lecture 13

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Dive into advanced LLM deployment strategies, covering optimization techniques, infrastructure scaling, and practical implementation methods for efficient large language model deployment.
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1 Lesons
1 hour 16 minutes
On-Demand
Free-Video
MIT HAN Lab

EfficientML.AI - Introduction to Efficient Machine Learning

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Master efficient machine learning techniques and optimization strategies for deploying ML models in resource-constrained environments, focusing on practical implementation methods.
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1 Lesons
1 hour 36 minutes
On-Demand
Free-Video
MIT HAN Lab

LLM Deployment Techniques - Lecture 13

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Dive into advanced LLM deployment strategies, covering optimization techniques, infrastructure scaling, and practical implementation methods for efficient large language model deployment in production environments.
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1 Lesons
1 hour 17 minutes
On-Demand
Free-Video
Discover AI

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

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Master LLM quantization techniques and implement efficient model compression using QLoRA, GPTQ, and Llamacpp, with practical guidance for running optimized LLMs locally across different platforms.
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1 Lesons
27 minutes
On-Demand
Free-Video
AI Bites

GGUF Quantization of Large Language Models Using LLAMA.cpp

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Master GGUF quantization techniques to run large language models efficiently on personal devices using LLAMA.cpp, enabling GPU-free inference and deployment on laptops, phones, and watches.
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7 Lesons
12 minutes
On-Demand
Free-Video
SNIAVideo

In-SRAM Computing for Generative AI and Large Language Models

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Explore innovative In-SRAM computing architecture and its role in reducing power consumption for large language models and generative AI in datacenter environments.
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1 Lesons
40 minutes
On-Demand
Free-Video
AI Bites

Quantization in Deep Learning: Types, Algorithms, and Implementation

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Explore quantization techniques in deep learning, from uniform to non-uniform approaches, and learn practical implementation strategies for optimizing large-scale neural networks.
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5 Lesons
13 minutes
On-Demand
Free-Video
AI Bites

Simple Quantization of LLMs - A Hands-on Guide to Absolute Max and Zero Point Methods

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Master practical LLM quantization techniques through hands-on implementation of Absolute Max and Zero Point methods, building foundational skills for advanced approaches like GPTQ and AWQ.
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1 Lesons
15 minutes
On-Demand
Free-Video
UofU Data Science

Faster and Cheaper LLMs with Weight and Key-value Cache Quantization

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Explore advanced techniques for optimizing Large Language Models through weight and key-value cache quantization methods to improve speed and reduce computational costs.
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1 Lesons
55 minutes
On-Demand
Free-Video
Federated Learning One World Seminar

On-Device Training Under 256KB Memory

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Discover cutting-edge techniques for on-device machine learning training with minimal memory requirements, presented by MIT's Song Han in this technical seminar on resource-efficient AI deployment.
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1 Lesons
59 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Efficient Tensor Representation for Deep Learning with TensorLy and PyTorch

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Explore tensor methods for deep learning, improving performance, speed, and robustness. Learn practical implementation using TensorLy-Torch for video and image classification tasks.
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1 Lesons
31 minutes
On-Demand
Free-Video
tinyML

TinyML Talks Germany - Neural Network Framework Using Emerging Technologies for Screening Diabetic

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Innovative framework using neural networks and emerging technologies for efficient screening of Diabetic Retinopathy, achieving high accuracy and significant improvements in energy consumption and latency.
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22 Lesons
55 minutes
On-Demand
Free-Video
tinyML

TinyML for All: Full-stack Optimization for Diverse Edge AI Platforms

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Full-stack optimization for edge AI platforms, addressing challenges in model compression, neural architecture rebalancing, and design primitives to deploy real-world AI on tiny microcontroller units despite limited resources.
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21 Lesons
34 minutes
On-Demand
Free-Video
tinyML

TinyML Talks - Software-Hardware Co-design for Tiny AI Systems

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Explore efficient AI models, hardware-friendly compression, and topology-aware design for resource-constrained systems. Learn cross-optimization techniques and distributed learning for scalable AI solutions.
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36 Lesons
1 hour 2 minutes
On-Demand
Free-Video
tinyML

On-Device Neural End-to-End Speech Recognition and Synthesis Algorithms - A Review

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Comprehensive review of on-device neural speech recognition and synthesis algorithms, exploring end-to-end models, optimization techniques, and their potential for on-device implementation.
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17 Lesons
49 minutes
On-Demand
Free-Video
tinyML

Extremely Low-Bit Quantization for Transformers - tinyML Asia 2021

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Explore extremely low-bit quantization techniques for Transformers, focusing on mixed precision strategies to optimize deployment on resource-limited devices without compromising accuracy.
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21 Lesons
28 minutes
On-Demand
Free-Video
tinyML

Putting AI on a Diet: TinyML and Efficient Deep Learning

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Efficient deep learning techniques for deploying AI on resource-constrained devices, including model compression, neural architecture search, and new design primitives for various applications.
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13 Lesons
33 minutes
On-Demand
Free-Video
tinyML

Networks Within Networks - Novel CNN Design Space Exploration for Resource-Limited Devices

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Explore novel CNN design for resource-limited devices using "network within network" paradigm. Learn automated method for compressing neural networks while preserving accuracy and meeting size constraints.
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5 Lesons
31 minutes
On-Demand
Free-Video
tinyML

Speech Recognition on Low Power Devices

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Explore multilingual speech recognition on low-power devices, covering model optimization techniques, neural network compression, and practical demonstrations for efficient implementation.
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21 Lesons
36 minutes
On-Demand
Free-Video
tinyML

The Model Efficiency Pipeline: Enabling Deep Learning Inference at the Edge

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Explore optimizing deep learning for edge devices through model efficiency techniques, including neural architecture search, pruning, quantization, and conditional processing.
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42 Lesons
50 minutes
On-Demand
Free-Video
tinyML

Advanced Network Quantization and Compression Using AIMET - tinyML Summit 2021

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Explore advanced network quantization and compression techniques using AIMET to optimize AI models for efficient deployment on end devices, addressing memory, compute, and energy constraints.
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20 Lesons
1 hour 34 minutes
On-Demand
Free-Video
tinyML

TinyML Talks Pakistan - SuperSlash - Unifying Design Space Exploration and Model Compression

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Unifying design space exploration and model compression for efficient deep learning accelerators on resource-constrained devices, optimizing off-chip memory access and model size.
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18 Lesons
53 minutes
On-Demand
Free-Video
Databricks

Conversational AI with Transformer Models - Building Blocks and Optimization Techniques

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Explore building conversational AI agents using transformer models, covering advantages over RNN/LSTM, knowledge distillation, and model compression techniques for efficient production deployment.
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21 Lesons
36 minutes
On-Demand
Free-Video
PyCon US

No Data? No Problem: Zero-Data Model Training with Foundational Models

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Explore zero-data model training using foundational models to create task-specific models without new data. Learn techniques for leveraging open-source tools in your projects.
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1 Lesons
55 minutes
On-Demand
Free-Video
CNCF [Cloud Native Computing Foundation]

Optimizing Knowledge Distillation Training With Volcano

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Explore knowledge distillation optimization using Volcano for efficient model compression, leveraging online GPU clusters and idle resources to enhance training throughput and flexibility.
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17 Lesons
29 minutes
On-Demand
Free-Video
SK AI SUMMIT 2024

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

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Discover how startups can differentiate through AI model optimization and edge computing, focusing on lightweight neural networks for wearable devices while maintaining high performance.
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1 Lesons
23 minutes
On-Demand
Free-Video
Toronto Machine Learning Series (TMLS)

Efficient Inference of Extremely Large Transformer Models

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Explore techniques for optimizing large transformer models, including compression, efficient attention, and parallelism for faster, smaller, and cost-effective inference.
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1 Lesons
28 minutes
On-Demand
Free-Video
MIT HAN Lab

TinyML and Efficient Deep Learning Computing - Course Summary

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Explore efficient machine learning techniques for deploying neural networks on resource-constrained devices, covering model compression, pruning, quantization, and more.
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3 Lesons
13 minutes
On-Demand
Free-Video
MIT HAN Lab

TinyML and Efficient Deep Learning Computing - Lecture 24: Course Summary

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Explore efficient machine learning techniques for deploying neural networks on resource-constrained devices. Gain hands-on experience with mobile AI and quantum machine learning.
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1 Lesons
13 minutes
On-Demand
Free-Video
MIT HAN Lab

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

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Explore activation-aware weight quantization for compressing and accelerating large language models. Learn about AWQ's innovative approach to LLM optimization.
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1 Lesons
19 minutes
On-Demand
Free-Video
MIT HAN Lab

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

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Explore Activation-aware Weight Quantization (AWQ) for efficient large language model deployment, reducing memory requirements while preserving performance across domains and modalities.
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1 Lesons
21 minutes
On-Demand
Free-Video
MIT HAN Lab

PockEngine: Sparse and Efficient Fine-tuning in a Pocket

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Explore sparse and efficient fine-tuning techniques for AI models with PockEngine, enhancing performance while reducing computational demands.
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1 Lesons
22 minutes
On-Demand
Free-Video
Shaw Talebi

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

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Explore three LLM compression techniques: quantization, pruning, and knowledge distillation. Learn to implement these methods with Python code examples for efficient model optimization.
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8 Lesons
24 minutes
On-Demand
Free-Video
Sam Witteveen

Llama 3.2 - Multimodal and Edge Computing Advancements

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Explore Llama 3.2's multimodal capabilities, edge computing potential, and performance benchmarks. Learn about its vision-tuned models, lightweight versions, and integration with Ollama.
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11 Lesons
13 minutes
On-Demand
Free-Video
Trelis Research

How to Quantize a Large Language Model with GGUF or AWQ

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Learn to quantize large language models using GGUF and AWQ. Explore techniques, compare methods, and gain practical skills for efficient model compression and deployment.
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8 Lesons
26 minutes
On-Demand
Free-Video
USENIX

Quant-LLM: Accelerating Large Language Model Serving via FP6-Centric Algorithm-System Co-Design

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Innovative GPU kernel design enables efficient 6-bit quantization for large language models, significantly improving inference speed while maintaining quality across various applications.
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1 Lesons
19 minutes
On-Demand
Free-Video
USC Information Sciences Institute

Composable Interventions for Language Models

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Explore composable interventions for language models, enhancing accuracy and mitigating harmful outputs. Learn about interactions between different intervention types and their practical implications.
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1 Lesons
52 minutes
On-Demand
Free-Video
tinyML

Memory Optimization Techniques for On-Device Large Language Models

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Discover advanced memory optimization techniques for deploying large language models on devices with limited resources, focusing on key-value caching and dynamic compression methods.
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1 Lesons
31 minutes
On-Demand
Free-Video
Linux Foundation

Unlocking Local LLMs with Quantization

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Discover how quantization optimizes LLMs for local deployment, from pre-training techniques to fine-tuning methods and inference optimization using cutting-edge approaches and community tools.
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1 Lesons
40 minutes
On-Demand
Free-Video
Oxen

Understanding 1-Bit Large Language Models - From Theory to Implementation

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Dive into the revolutionary concept of 1-bit Large Language Models, exploring how weights can be represented with simple integers (0, 1, -1) instead of complex floating-point numbers.
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16 Lesons
47 minutes
On-Demand
Free-Video
echohive

OpenAI Model Distillation and Evals Explained

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Explore OpenAI's model distillation techniques and evaluation methods for enhanced AI understanding and implementation.
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1 Lesons
47 minutes
On-Demand
Free-Video
MIT HAN Lab

AI Model Efficiency Toolkit (AIMET) - Lecture 25

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Explore AI model efficiency techniques with Qualcomm's toolkit. Learn about adaptive rounding, autocon, and transformer quantization for optimized inference and training on resource-constrained devices.
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17 Lesons
45 minutes
On-Demand
Free-Video
MIT HAN Lab

AI Model Efficiency Toolkit (AIMET) - Lecture 25

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Explore AI model efficiency techniques with Qualcomm AI Research. Learn about AIMET, community research, core technologies, and industry applications for optimizing deep learning models.
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21 Lesons
45 minutes
On-Demand
Free-Video
MIT HAN Lab

Pruning and Sparsity in Machine Learning - Part I

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Explore pruning and sparsity techniques in machine learning, focusing on improving model efficiency without sacrificing performance. Learn key concepts and practical applications.
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1 Lesons
58 minutes
On-Demand
Free-Video
Unify

SliceGPT Explained - Compressing Large Language Models

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Explore SliceGPT's innovative approach to compressing large language models by deleting rows and columns, maintaining high performance while reducing parameters significantly.
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1 Lesons
55 minutes
On-Demand
Free-Video
Unify

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

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Explore groundbreaking research on BitNet b1.58, a ternary parameter model matching full-precision Transformers while offering improved cost-effectiveness and defining new LLM scaling laws.
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1 Lesons
58 minutes
On-Demand
Free-Video
The Machine Learning Engineer

LLM Quantization: Why Size Matters

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Explore LLM quantization techniques, their impact on model size, and practical applications in training, fine-tuning, and inference. Gain insights into scaling laws and optimization strategies.
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1 Lesons
47 minutes
On-Demand
Free-Video
Neural Magic

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

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Explore sparse models' transfer performance and efficiency gains in image and language tasks, outperforming dense models even at high sparsities.
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1 Lesons
50 minutes
On-Demand
Free-Video
Neural Magic

Sparse Training of Neural Networks Using AC/DC

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Explore AC/DC, a novel sparse training algorithm for DNNs. Learn its benefits, implementation, and deployment for improved performance and accuracy in deep learning.
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1 Lesons
42 minutes
On-Demand
Free-Video
Neural Magic

Applying Second-Order Pruning Algorithms for SOTA Model Compression

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Explore second-order pruning algorithms for model compression, achieving higher sparsity while maintaining accuracy. Learn to apply these techniques to your ML projects for improved efficiency.
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17 Lesons
42 minutes
On-Demand
Free-Video
Neural Magic

Pruning and Quantizing ML Models With One Shot Without Retraining

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Adapt advanced pruning and quantization methods for ML models, achieving 4X speedup with 99% accuracy in minutes, using Computer Vision and NLP examples.
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1 Lesons
53 minutes
On-Demand
Free-Video
Neural Magic

Unlock Faster and More Efficient LLMs with SparseGPT - Neural Magic

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Optimize LLMs with SparseGPT: prune and quantize models for efficient CPU deployment at GPU speeds. Explore one-shot compression, benchmarks, and practical implementation.
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15 Lesons
42 minutes
On-Demand
Free-Video
Scalable Parallel Computing Lab, SPCL @ ETH Zurich

Efficient AI: From Supercomputers to Smartphones

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Explore techniques for efficient AI training and inference, from data ingestion to model compression, enabling powerful models on supercomputers and smartphones.
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1 Lesons
45 minutes
On-Demand
Free-Video
MITCBMM

SGD and Weight Decay Secretly Compress Your Neural Network

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Explore how SGD and weight decay techniques compress neural networks, enhancing efficiency and performance in deep learning models.
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1 Lesons
55 minutes
On-Demand
Free-Video
MIT HAN Lab

MCUNet: Tiny Neural Network Design for Microcontrollers - Lecture 11

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Explore algorithm and system co-design for tiny neural network inference on microcontrollers, focusing on TinyML, TinyNAS, and TinyEngine for efficient deep learning applications.
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1 Lesons
1 hour 7 minutes
On-Demand
Free-Video
MIT HAN Lab

MCUNet: Tiny Neural Network Design for Microcontrollers - Lecture 11

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Explore algorithm and system co-design for tiny neural network inference on microcontrollers, focusing on TinyML, TinyNAS, and TinyEngine for efficient deep learning applications.
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1 Lesons
1 hour 7 minutes
On-Demand
Free-Video
MIT HAN Lab

Neural Architecture Search (Part II) - Lecture 8

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Explore neural architecture search techniques, including hardware-aware approaches and joint optimization of hardware and neural architectures. Learn applications and advancements in efficient deep learning design.
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1 Lesons
1 hour 13 minutes
On-Demand
Free-Video
MIT HAN Lab

Quantization in Neural Networks - Lecture 5

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Explore neural network quantization, including numeric data types, K-means-based quantization, and linear quantization for efficient deep learning on resource-constrained devices.
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1 Lesons
1 hour 12 minutes
On-Demand
Free-Video
MIT HAN Lab

Neural Architecture Search for Efficient Deep Learning - Lecture 9

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Explore advanced neural architecture search techniques for efficient deep learning on resource-constrained devices. Gain insights into model optimization for mobile and IoT applications.
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1 Lesons
1 hour 7 minutes
On-Demand
Free-Video
MIT HAN Lab

Neural Architecture Search (Part II) - Lecture 8

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Explore neural architecture search techniques, including hardware-aware approaches and joint optimization of hardware and neural architectures. Learn applications and advancements in efficient deep learning design.
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1 Lesons
1 hour 13 minutes
On-Demand
Free-Video
VinAI

Implicit Deep Learning - Seminar Series

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Explore implicit deep learning models, their state-space representation, training problems, and applications in parameter reduction, feature elimination, and mathematical reasoning tasks.
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1 Lesons
1 hour 9 minutes
On-Demand
Free-Video
HUJI Machine Learning Club

Efficiency in the Age of Large Scale Models - Designing and Optimizing Deep Learning Systems

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Explore the challenges and solutions of large-scale AI models, from theoretical foundations to practical efficiency improvements, with insights on reducing computational costs while maintaining performance in modern machine learning.
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1 Lesons
1 hour 4 minutes
On-Demand
Free-Video
MIT HAN Lab

Quantization for Efficient Machine Learning - Part I - Lecture 5

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Explore quantization techniques for efficient machine learning, covering fundamental concepts and practical applications in neural network optimization.
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1 Lesons
1 hour 15 minutes
On-Demand
Free-Video
MIT HAN Lab

EfficientML.ai - Quantization Part I - Lecture 5

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Explore quantization techniques for efficient machine learning, focusing on part I of the topic in this MIT lecture by Prof. Song Han.
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1 Lesons
1 hour 15 minutes
On-Demand
Free-Video
MIT HAN Lab

EfficientML.ai Lecture - Introduction to Efficient Machine Learning

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Explore cutting-edge techniques for optimizing machine learning models, focusing on efficiency and performance in AI applications.
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1 Lesons
1 hour 26 minutes
On-Demand
Free-Video
MIT HAN Lab

Pruning and Sparsity in Machine Learning - Part II (EfficientML.ai Lecture 4)

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Explore advanced pruning techniques and sparsity concepts for efficient machine learning, focusing on practical applications and cutting-edge research.
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1 Lesons
1 hour 18 minutes
On-Demand
Free-Video
MIT HAN Lab

Pruning and Sparsity in Machine Learning - Lecture 3

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Explore pruning and sparsity techniques for efficient machine learning, focusing on reducing model size and computational requirements while maintaining performance.
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1 Lesons
1 hour 9 minutes
On-Demand
Free-Video
MIT HAN Lab

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

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Explore pruning and sparsity techniques for efficient machine learning, focusing on reducing model size and computational requirements while maintaining performance.
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1 Lesons
1 hour 9 minutes
On-Demand
Free-Video
MIT HAN Lab

EfficientML.ai: Introduction to Efficient Machine Learning - Lecture 1

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Explore efficient machine learning techniques and their applications in this comprehensive introduction to EfficientML.ai from MIT's Prof. Song Han.
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1 Lesons
1 hour 17 minutes
On-Demand
Free-Video
MIT HAN Lab

EfficientML.ai: Introduction to Efficient Machine Learning - Lecture 1

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Explore efficient machine learning techniques and their applications in this introductory lecture from MIT's EfficientML course, led by Prof. Song Han.
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1 Lesons
1 hour 17 minutes
On-Demand
Free-Video
MIT HAN Lab

Quantum Machine Learning - Lecture 22

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Explore quantum machine learning fundamentals, bridging quantum computing and AI. Gain insights into this emerging field's potential to revolutionize computational approaches.
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1 Lesons
1 hour 9 minutes
On-Demand
Free-Video
MIT HAN Lab

Efficient Transformers - Lecture 20

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Explore efficient transformer architectures for optimized deep learning on resource-constrained devices. Learn techniques to enhance performance and reduce computational demands.
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1 Lesons
1 hour 18 minutes
On-Demand
Free-Video
MIT HAN Lab

Efficient Transformers - Lecture 20

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Explore efficient transformer architectures for optimized deep learning on resource-constrained devices. Learn techniques to enhance performance and reduce computational demands.
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1 Lesons
1 hour 18 minutes
On-Demand
Free-Video
MIT HAN Lab

TinyEngine - Efficient Training and Inference on Microcontrollers - Lecture 17

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Explore TinyEngine library for efficient neural network training and inference on microcontrollers. Learn techniques to optimize deep learning for resource-constrained devices.
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1 Lesons
1 hour 15 minutes
On-Demand
Free-Video
MIT HAN Lab

LLM Post-Training Techniques - Lecture 14

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Dive into advanced LLM post-training techniques and deployment strategies, exploring optimization methods and practical implementation approaches for efficient language model deployment.
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1 Lesons
1 hour 19 minutes
On-Demand
Free-Video
MIT HAN Lab

On-Device Training and Transfer Learning - Lecture 15

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Explore on-device training and transfer learning techniques, focusing on memory bottlenecks, efficient algorithms like TinyTL, and system support for optimized on-device training.
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1 Lesons
1 hour 16 minutes
On-Demand
Free-Video
MIT HAN Lab

On-Device Training and Transfer Learning - Lecture 15

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Explore on-device training and transfer learning techniques, focusing on memory bottlenecks, efficient algorithms like TinyTL, and system support for optimized on-device training.
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1 Lesons
1 hour 16 minutes
On-Demand
Free-Video
MIT HAN Lab

TinyEngine - Efficient Training and Inference on Microcontrollers - Lecture 17

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Explore TinyEngine library for efficient neural network training and inference on microcontrollers. Learn techniques to optimize deep learning for resource-constrained devices.
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1 Lesons
1 hour 15 minutes
On-Demand
Free-Video
MIT HAN Lab

Efficient Video Understanding and Generative Models - Lecture 19

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Explore efficient video understanding and generative models, focusing on techniques for deploying neural networks on resource-constrained devices and optimizing deep learning applications.
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1 Lesons
1 hour 38 minutes
On-Demand
Free-Video
MIT HAN Lab

Efficient Video Understanding and Generative Models - Lecture 19

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Explore efficient video understanding and generative models, focusing on techniques for deploying neural networks on resource-constrained devices and optimizing deep learning applications.
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1 Lesons
1 hour 38 minutes
On-Demand
Free-Video
MIT HAN Lab

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

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Explore advanced pruning techniques and sparsity concepts for efficient machine learning, focusing on practical applications and cutting-edge research in neural network optimization.
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1 Lesons
1 hour 18 minutes
On-Demand
Free-Video
MIT HAN Lab

Quantization for Efficient Machine Learning - Part II

0 rewiews
Explore advanced quantization techniques for efficient machine learning, including post-training and quantization-aware training methods.
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1 Lesons
1 hour 11 minutes
On-Demand
Free-Video
MIT HAN Lab

MCUNet: TinyML on Microcontrollers - Lecture 10

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Explore MCUNet for TinyML on microcontrollers, covering efficient neural architecture design and deployment strategies for resource-constrained devices.
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1 hour 1 minute
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MIT HAN Lab

EfficientML.ai: Introduction to Efficient Machine Learning - Lecture 1

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Explore efficient machine learning techniques and their applications in this introductory lecture from MIT's EfficientML course.
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1 hour 6 minutes
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MIT HAN Lab

EfficientML.ai: Introduction to Efficient Machine Learning - Lecture 1

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Explore efficient machine learning techniques and their applications in this comprehensive introduction to EfficientML.ai from MIT's Prof. Song Han.
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1 hour 6 minutes
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MIT HAN Lab

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

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Explore advanced pruning techniques and sparsity concepts for efficient machine learning, focusing on practical applications and cutting-edge research in neural network optimization.
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1 hour 9 minutes
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MIT HAN Lab

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

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Explore advanced pruning techniques and sparsity concepts for efficient machine learning, focusing on practical applications and cutting-edge research.
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1 hour 9 minutes
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MIT HAN Lab

EfficientML.ai - Quantization Part I - Lecture 5

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Explore quantization techniques for efficient machine learning, covering fundamental concepts and practical applications in neural network optimization.
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1 hour 7 minutes
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MIT HAN Lab

EfficientML.ai - Quantization Part I - Lecture 5

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Explore quantization techniques for efficient machine learning, covering fundamental concepts and practical applications in neural network optimization.
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1 hour 7 minutes
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MIT HAN Lab

EfficientML.ai - Quantization Part II - Lecture 6

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Explore advanced quantization techniques for efficient machine learning, including post-training and quantization-aware training methods.
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1 hour 9 minutes
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