Top AI Courses from NVIDIA

As AI continues to gain popularity across industries, NVIDIA stands at the forefront, providing cutting-edge technologies and solutions. Their courses on various AI topics empower individuals with the knowledge and skills to harness AI’s potential effectively. This article lists the top AI courses NVIDIA provides, offering comprehensive training on advanced topics like generative AI, graph neural networks, and diffusion models, equipping learners with essential skills to excel in the field.

Getting Started with Deep Learning

This course teaches the fundamentals of deep learning through hands-on exercises in computer vision and natural language processing. Participants will train models from scratch, use pre-trained models, and apply techniques like data augmentation and transfer learning to achieve accurate results.

Generative AI Explained

This course provides an overview of Generative AI, its concepts, applications, challenges, and opportunities. Participants will gain a basic understanding of how Generative AI works and how to use various tools built on this technology.

Disaster Risk Monitoring Using Satellite Imagery

This course teaches how to build and deploy deep learning models to detect flood events using satellite imagery. Participants will learn to implement machine learning workflows, process large data with accelerated tools, and deploy models for real-time analysis using NVIDIA’s tools and frameworks.

Accelerating End-to-End Data Science Workflows

This course teaches developers to build and execute end-to-end GPU-accelerated data science workflows using RAPIDS libraries. The course teaches how to perform fast data preparation, machine learning, graph analysis, and visualization, significantly improving productivity and efficiency in handling large datasets.

Building Real-Time Video AI Applications

This course teaches how to build and deploy AI-based video analytics solutions using NVIDIA’s tools. Students will learn to construct streaming analytics pipelines, deploy pre-trained models, apply transfer learning for custom models, and optimize video AI application performance.

Generative AI with Diffusion Models

This course delves into the fundamentals of diffusion models, which are popular for text-to-image pipelines in various applications such as creative content generation and drug discovery. It covers how to build and improve U-Nets for image generation, control output with context embeddings, and generate images from text prompts using the CLIP neural network.

Getting Started with Image Segmentation

This course teaches image segmentation using MRI images to measure heart parts, covering TensorFlow tools and performance metrics. It also covers how to set up deep learning workflows for various computer vision tasks.

Introduction to Graph Neural Networks

This course teaches the fundamentals of graph neural networks (GNNs), their applications, and how to build and train GNN models. The course covers important graph concepts, neural network applications to graphs, and practical uses across various industries.

Building RAG Agents with LLMs

This course explores the deployment and efficient implementation of large language models (LLMs) for enhanced productivity. Participants will learn to design dialog management systems, utilize embeddings for content retrieval, and implement advanced LLM pipelines using tools like LangChain and Gradio.

Introduction to Transformer-Based Natural Language Processing

This course teaches how Transformer-based large language models (LLMs) are used in modern NLP applications. It covers using these models for tasks such as text classification, named-entity recognition (NER), author attribution, and question answering, providing a foundational understanding of leveraging pre-trained LLMs for various NLP tasks.

Prompt Engineering with LLaMA-2

This course covers the prompt engineering techniques that enhance the capabilities of large language models (LLMs) like LLaMA-2. Students will learn to write precise prompts, edit system messages, and incorporate prompt-response history to create AI assistant and chatbot behavior.

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