Learn ModeNVIDIA AI Technology Fundamentals
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Terms studied in this round
NVIDIA GPU
Graphics Processing Units developed by NVIDIA, repurposed for parallel processing crucial for AI/ML workloads, enabling faster computations for complex algorithms.CUDA
Compute Unified Device Architecture, NVIDIA's parallel computing platform and programming model that allows software developers to use a GPU for general-purpose processing, essential for AI development.Tensor Cores
Specialized processing units within NVIDIA GPUs (e.g., Volta, Ampere architectures) designed to accelerate matrix multiplication operations, fundamental to deep learning and AI model training.cuDNN
CUDA Deep Neural Network library, a GPU-accelerated library of primitives for deep neural networks, optimized for popular deep learning frameworks like TensorFlow and PyTorch.NVIDIA DGX Systems
A line of NVIDIA-built AI supercomputers, ranging from individual workstations to large-scale data center systems, designed for accelerated deep learning research and development.NVIDIA Jetson Platform
NVIDIA's platform for AI at the edge, consisting of small, powerful compute modules and developer kits for embedded systems, robotics, and IoT applications requiring on-device AI.NVIDIA Clara
An AI computing platform for healthcare and life sciences, designed to accelerate medical imaging, genomics, and drug discovery using AI technologies.NVIDIA Isaac
A comprehensive robotics platform that includes SDKs, simulators, and hardware for developing, training, and deploying AI-powered robots and automation solutions.NVIDIA DRIVE
An end-to-end platform for autonomous vehicles, encompassing hardware (like the DRIVE AGX Orin), software, and development tools for self-driving technology and intelligent cockpits.NVIDIA AI Enterprise (NVAIE)
An end-to-end cloud-native suite of AI and data science software optimized for NVIDIA-certified systems, helping enterprises deploy, manage, and scale AI faster.