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Exam 3 - NVIDIA-Certified Associate: Generative AI Multimodal (NCA-GENM)
Data Analysis for NVIDIA-Certified Associate Certification: Extracting insights from large datasets using data mining, visualization, cleaning, and transformation, Attention maps in multimodal settings and Identify relationships and trends or any factors that could affect the results of research.
Exam 4 - NVIDIA-Certified Associate: Generative AI Multimodal (NCA-GENM)
Experimentation for Generative AI Multimodal (NCA-GENM): Assist in developing and testing multimodal AI models, manage and preprocess data from various sources, use multimodal models to improve explainability, test data quality and consistency in a multimodal setting, and test AI models to ensure their accuracy and effectiveness.
Exam 5 - NVIDIA-Certified Associate: Generative AI Multimodal (NCA-GENM)
Multimodal Data for NVIDIA Generative AI: Deployment and evaluations of model scalability, performance, and reliability, build LLM use cases such as retrieval-augmented generation (RAG), chatbots, and summarizers, and understand the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
Exam 7 - NVIDIA-Certified Associate: Generative AI Multimodal (NCA-GENM)
Performance Optimization: Enhance computational efficiency and improve the accuracy of outputs in AI models, optimize the performance of AI models, including tuning hyperparameters, and assist in model training and training optimization.
Exam 8 - NVIDIA-Certified Associate: Generative AI Multimodal (NCA-GENM)
Software Development for NVIDIA-Certified Associate - Generative AI Multimodal: Build a U-Net to generate images from pure noise and as a type of autoencoder, generate images from English text prompts using CLIP, and use CLIP to train a text-to-image diffusion model.
Video Analysis and AI Python App Development with OpenAI Vision
Video Analysis and AI Python App Development with OpenAI Vision