Beyond CUDA: GPU Accelerated Machine Learning on Cross-Vendor Graphics Cards with Vulkan Kompute
Explore GPU-accelerated machine learning across multiple vendors using Vulkan Kompute. Learn parallel processing options, Vulkan basics, and implement a linear regression example on diverse graphics cards.
Unlock AI Innovation with Azure AI Infrastructure
Explore Azure's GPU-accelerated clusters for AI model training and inference, integrating with Azure Machine Learning and third-party solutions to drive AI innovation.
Wayland Driver for Wine: Development Journey and Implementation Challenges
Explore the development journey of Wine's Wayland driver, from experimental beginnings to full GPU acceleration support, including technical challenges and solutions for Windows-Wayland compatibility.
Advanced Color Management on Linux with AMD and Steam Deck
Dive into AMD display driver architecture and color management implementation for Linux systems, focusing on HDR capabilities and SteamOS integration for enhanced gaming experiences.
Accelerate Your GenAI Model Inference with Ray and Kubernetes
Explore serving large GenAI models efficiently using KubeRay on Kubernetes with hardware accelerators. Learn to scale workloads, optimize performance, and reduce costs for production-ready AI inference.
Introduction to Coding Neural Networks with PyTorch and Lightning
Aprenda a codificar redes neurais eficientemente com PyTorch e Lightning, simplificando o processo, melhorando a portabilidade e otimizando a taxa de aprendizagem automaticamente.
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU
Explore GPU-accelerated privacy-preserving machine learning techniques for enhanced performance and scalability in image classification and private training.
GPU-Acceleration for PyTorch on M1 Macs - Using with BERT
Explore GPU acceleration for PyTorch on M1 Macs, including installation, implementation with BERT, and best practices for training large language models on Mac hardware.
Adding an Interactive 3D Camera System - Ray Tracing Series
Learn to implement an interactive 3D camera system for ray tracing, covering camera mechanics, class creation, ray direction calculations, and integration into a ray tracing application.
PyTorch Datasets, DataLoaders and Transforms - GPU Series Part 3
Explore PyTorch's data handling tools: Datasets, DataLoaders, and Transforms. Learn to prepare, visualize, and optimize data for GPU-accelerated deep learning using Fashion-MNIST dataset.
Introduction to Tensors - PyTorch with GPU Series Part 2
Explore tensor initialization, GPU usage, operations, memory sharing, and profiling in PyTorch for efficient deep learning model development.
Scaling NumPy Applications from 1 CPU to Thousands of GPUs - IPAM at UCLA
Discover cuNumeric: a NumPy replacement enabling Python applications to scale across multiple nodes and leverage GPU acceleration for enhanced performance and dataset handling.
Building a Winning Deep Learning Recommender System - Grandmaster Series Episode 5
Learn how Kaggle Grandmasters built a winning Deep Learning Recommender System for Booking.com, using matrix factorization, ensemble models, and data augmentation techniques. Gain insights into GPU-accelerated recommender systems.
Efficient Data Loading Using DALI for Deep Learning Applications
Accelerate deep learning applications with efficient data loading pipelines using NVIDIA's DALI library. Explore augmentation, documentation, and performance optimization techniques.
DeepStream SDK - Accelerating Real-Time AI-Based Video and Image Analytics
Accelerate real-time AI-based video and image analytics with DeepStream SDK. Learn about flexible deployment, architecture, and applications in retail, smart cities, manufacturing, and logistics.
Boosted Trees and Deep Neural Networks for Better Recommender Systems
Explore GPU-accelerated boosted trees and deep neural networks for building winning recommender systems, featuring insights from Kaggle Grandmasters on the ACM RecSys Challenge.
Getting Started with Caffe - Deep Learning Framework Introduction - Class 3
Learn to define, train, and deploy Deep Neural Networks using Caffe framework. Covers data management, network definition, training monitoring, and deployment for classification or feature extraction.
Advanced Rendering Solutions - NVIDIA ARC and Frameworks
Explore NVIDIA's advanced rendering solutions, including mental ray, iray, and OptiX. Gain insights into integrating and accelerating graphics pipelines for cutting-edge visual effects and simulations.
Visual Rendering Techniques - Session 26
Explore advanced techniques in visual rendering, covering cutting-edge methods for creating realistic and immersive digital imagery in various applications.
Path Tracing - Introduction to Ray Tracing Series
Explore path tracing in ray tracing, including accumulation techniques and sample gathering. Learn to create photo-realistic digital imagery with optimized C++ implementation and GPU acceleration.
GPU Accelerated Deep Learning for Scala 3 with Storch
Discover Storch, a new PyTorch-based library for GPU-accelerated deep learning in Scala 3. Learn about tensor operations, automatic differentiation, and neural network APIs for building enterprise-grade machine learning applications.