LLM Post-Training - Lecture 14
Dive into advanced LLM post-training techniques and methodologies with Prof. Song Han, exploring efficient machine learning strategies for optimizing large language models.
MCUNet: TinyML on Microcontrollers - Lecture 10
Explore MCUNet for TinyML on microcontrollers, covering efficient neural architecture design and deployment strategies for resource-constrained devices.
On-Device Training and Transfer Learning - Lecture 19
Explore on-device training and transfer learning techniques for efficient machine learning, focusing on practical applications and cutting-edge methodologies.
On-Device Training and Transfer Learning - Lecture 19
Explore on-device training and transfer learning techniques for efficient machine learning, focusing on practical applications and cutting-edge methodologies.
Qiskit, PennyLane QML, and QiML Demos - Thanksgiving Eve
Explore Qiskit and PennyLane QML/QiML demos to improve runtimes, losses, and accuracies. Learn quantum computational building blocks and differentiable programming for medical R&D applications.
The Art and Science of Training Large Language Models
Explore challenges and strategies in training large language models with insights from MosaicML experts. Gain practical knowledge on model optimization, data complexities, and efficient AI development.
EfficientML.AI Lecture - Machine Learning Efficiency and Optimization
Master efficient machine learning techniques and optimization strategies for deploying ML models in resource-constrained environments, focusing on practical implementation methods.
EfficientML.AI - Introduction to Efficient Machine Learning
Master efficient machine learning techniques and optimization strategies for deploying ML models in resource-constrained environments, focusing on practical implementation methods and performance enhancement.
GAN, Video, and Point Cloud Processing in Machine Learning - Lecture 17
Dive into advanced machine learning concepts covering GANs, video processing, and point cloud analysis with MIT's Prof. Song Han, exploring cutting-edge techniques for efficient ML implementation.
GAN, Video, and Point Cloud Processing in Machine Learning - Lecture 17
Explore advanced machine learning concepts including GANs, video processing, and point cloud analysis through MIT's comprehensive examination of efficient ML implementations.
BitNet.cpp - Inferencia de Modelos de hasta 100B en CPU
Descubre cómo implementar inferencia de modelos BitNet con precisión de 1 bit usando el framework de Microsoft para CPU, permitiendo el despliegue eficiente de modelos de hasta 100B de parámetros.
BitNet.cpp - CPU Inference Framework for 1-bit Large Language Models
Explore BitNet CPP's optimized framework for efficient 1-bit LLM inference, featuring specialized kernels for fast, lossless CPU-based processing of 1.58-bit models.
Converting Qwen2-VL 2B Model to OpenVino IR Format for CPU Inference
Learn how to convert and optimize Qwen2-VL 2B model for CPU inference using OpenVino IR format, with practical implementation steps and performance insights.
Fine-tuning Azure OpenAI Service Models for Custom AI Applications
Master the techniques for fine-tuning Azure OpenAI models through hands-on guidance, best practices, and optimization strategies to enhance AI performance for custom applications and specific use cases.
Optimizing Phi and Small Language Models Performance Through Customization
Master fine-tuning techniques for Phi and Small Language Models (SLMs), exploring optimization strategies and customization approaches to enhance AI performance for specific use cases.
Converting T5 Large Models to ONNX Format and 8-bit Quantization with MLflow and Optimum
Master model optimization techniques by converting T5 Large to ONNX format and implementing 8INT quantization using Hugging Face Transformers Optimum, with MLflow integration for text summarization tasks.
EfficientML.AI - Introduction to Efficient Machine Learning
Master efficient machine learning techniques and optimization strategies for deploying ML models in resource-constrained environments, focusing on practical implementation and performance tuning.
Custom, Targeted, and Neurosymbolic AI Models for Healthcare Applications
Explore the critical role of specialized AI models in healthcare, focusing on neurosymbolic approaches and compact solutions for improved medical applications and outcomes.
EfficientML.AI - Introduction to Efficient Machine Learning
Master efficient machine learning techniques and optimization strategies for deploying ML models in resource-constrained environments, focusing on practical implementation methods and performance enhancement.