Model Compression Courses and Certifications

Quantization Fundamentals with Hugging Face

Learn to compress and optimize generative AI models through quantization techniques, making them more efficient and accessible for various devices while minimizing performance loss.

• Coursera
• 1 hour 30 minutes
• On-Demand
• Paid Course

Quantization in Depth

Build and customize linear quantization methods to compress model weights, balance performance and space tradeoffs, and implement techniques for 8-bit and 2-bit quantization in PyTorch.

• Coursera
• 1 hour 30 minutes
• On-Demand
• Paid Course

tinyML

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

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.

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

tinyML

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

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.

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

tinyML

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

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.

• YouTube
• 1 hour 2 minutes
• On-Demand
• Free Video

tinyML

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

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.

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

tinyML

Extremely Low-Bit Quantization for Transformers - tinyML Asia 2021

Explore extremely low-bit quantization techniques for Transformers, focusing on mixed precision strategies to optimize deployment on resource-limited devices without compromising accuracy.

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

tinyML

Putting AI on a Diet: TinyML and Efficient Deep Learning

Efficient deep learning techniques for deploying AI on resource-constrained devices, including model compression, neural architecture search, and new design primitives for various applications.

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

tinyML

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

Unifying design space exploration and model compression for efficient deep learning accelerators on resource-constrained devices, optimizing off-chip memory access and model size.

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

tinyML

Advanced Network Quantization and Compression Using AIMET - tinyML Summit 2021

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.

• YouTube
• 1 hour 34 minutes
• On-Demand
• Free Video

tinyML

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

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.

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

tinyML

Speech Recognition on Low Power Devices

Explore multilingual speech recognition on low-power devices, covering model optimization techniques, neural network compression, and practical demonstrations for efficient implementation.

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

tinyML

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

Explore optimizing deep learning for edge devices through model efficiency techniques, including neural architecture search, pruning, quantization, and conditional processing.

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

HuggingFace

Few-Shot Learning in Production

Learn to train and deploy efficient few-shot language models using SetFit, knowledge distillation, and quantization techniques. Explore accelerated inference and deployment strategies with Hugging Face and Intel AI.

• YouTube
• 1 hour 22 minutes
• On-Demand
• Free Video

TensorFlow Lite for Edge Devices - Tutorial

Explore TensorFlow Lite for efficient on-device inference. Learn edge computing, model conversion, quantization, and compression techniques for deploying deep learning models on resource-constrained devices.

• freeCodeCamp
• On-Demand
• Free Video