NVIDIA-Certified Associate: Generative AI Multimodal: key terms

Flashcards for this exam NVIDIA-Certified Associate: Generative AI Multimodal guide Class dates

• AutoencoderA neural network trained to reconstruct its input through a compressed bottleneck representation.Intermediate
• CLIPContrastive Language-Image Pre-training: OpenAI's model that embeds images and text into a shared vector space.Advanced
• Convolutional Neural Network (CNN)A neural network that uses convolutional filters to detect spatial patterns, primarily for images.Intermediate
• Data AugmentationExpanding a training set by creating modified copies of existing examples.Intermediate
• Diffusion ModelA generative model that learns to create data by reversing a gradual noising process.Intermediate
• Edge AIRunning AI inference on devices near the data source, such as cameras, robots or phones, rather than in the cloud.Intermediate
• Generative Adversarial Network (GAN)A generative model in which a generator and a discriminator are trained against each other.Intermediate
• Image ClassificationA computer vision task that assigns a label to an entire image.Foundational
• Image SegmentationA computer vision task that labels each pixel of an image with a class or object instance.Intermediate
• Latent SpaceA compressed, learned representation space where similar inputs map to nearby points.Intermediate
• Low-Rank Adaptation (LoRA)A PEFT method that freezes base weights and trains small low-rank matrices added to selected layers.Intermediate
• Multimodal EmbeddingAn embedding that places different modalities, such as images and text, in the same vector space.Intermediate
• Multimodal ModelA model that can process or generate more than one data type, such as text, images, audio and video.Foundational
• NVIDIA CosmosNVIDIA's platform of world foundation models for generating and understanding physical-world video and data.Advanced
• NVIDIA NeMoNVIDIA's framework and microservices for building, customizing, evaluating and guarding generative AI models and agents.Intermediate
• NVIDIA OmniverseNVIDIA's platform for building and operating physically accurate 3D simulations and digital twins based on OpenUSD.Advanced
• Object DetectionA computer vision task that identifies and locates multiple objects in an image with bounding boxes.Intermediate
• Optical Character Recognition (OCR)Technology that extracts machine-readable text from images or scanned documents.Foundational
• Speech-to-Text (STT)Converting spoken audio into written text; also called automatic speech recognition (ASR).Foundational
• Stable DiffusionA family of open-weight latent diffusion models from Stability AI for text-to-image generation.Intermediate
• Synthetic DataArtificially generated data used to train, fine-tune or evaluate models.Intermediate
• Text-to-ImageGenerating images from natural-language descriptions.Foundational
• Text-to-Speech (TTS)Generating natural-sounding spoken audio from text.Foundational
• Variational Autoencoder (VAE)A generative autoencoder that learns a probabilistic latent space from which new samples can be drawn.Advanced
• Vision Transformer (ViT)A transformer that processes an image as a sequence of fixed-size patches embedded like tokens.Advanced
• Vision-Language Model (VLM)A model that jointly understands images and text, for tasks such as visual question answering and captioning.Intermediate