LLM

Learn how to apply and fine-tune a Transformer-based Deep Learning model to Natural Language Processing (NLP) tasks.

Learn how deep learning works through hands-on exercises in computer vision and natural language processing.

Whether companies are manufacturing semiconductor chips, airplanes, automobiles, smartphones, or food or beverages, quality and throughput are key benefits of optimization.

Modern deep learning challenges leverage increasingly larger datasets and more complex models.

In this no-coding course, learn Generative AI concepts and applications, as well as the challenges and opportunities in this exciting field.

Thanks to improvements in computing power and scientific theory, generative AI is more accessible than ever before.

Learn how Transformers are used as the building blocks of modern large language models (LLMs).

In this course you'll learn the end-to-end development workflow for generating synthetic data using Transformers, including data preprocessing, model pre-training, fine-tuning, inference, and evaluation.

Take a deeper dive into denoising diffusion models, which are a popular choice for text-to-image pipelines, with applications in creative content generation, data augmentation, simulation and planning, anomaly detection, drug discovery, personalized recommendations, and more.

About This Course Very large deep neural networks (DNNs), whether applied to natural language processing (e.g., GPT-3), computer vision (e.g., huge Vision Transformers), or speech AI (e.g., Wave2Vec 2) have certain properties that set them apart from their smaller counterparts. As DNNs become larger and are trained on progressively larger datasets, they can adapt to new tasks with just a handful of training examples, accelerating the route toward general artificial intelligence. Training models that contain tens to hundreds of billions of parameters on vast datasets isn’t trivial and requires a unique combination of AI, high-performance computing (HPC), and systems knowledge. In this workshop, participants will learn how to: Train neural networks across multiple servers Use techniques such as activation checkpointing, gradient accumulation, and various forms of model parallelism to overcome the challenges associated with large-model memory footprint Capture and understand training performance characteristics to optimize model architecture Deploy very large multi-GPU models to production using NVIDIA Triton™ Inference Server The goal of this course is to demonstrate how to train the largest of neural networks and deploy them to production. Requirements Familiarity with: Good understanding of PyTorch Good understanding of deep learning and data parallel training concepts Practice with deep learning and data parallel are useful, but optional Tools, libraries, frameworks used: PyTorch, Megatron-LM, DeepSpeed, Slurm, Triton Inference Server Related Training Building Transformer-Based Natural Language Processing Applications Learn how to use Transformer-based natural language processing models for text classification tasks, such as categorizing documents. Fundamentals of Deep Learning for Multi-GPUs echniques for training deep neural networks on multi-GPU technology to shorten the training time required for data-intensive applications. For additional hands-on training through the NVIDIA Deep Learning Institute, visit www.nvidia.com/dli .

Agents powered by large language models (LLMs) have shown great retrieval capability for using tools, looking at documents, and plan their approaches.

Agents powered by large language models (LLMs) have shown great retrieval capability for using tools, looking at documents, and plan their approaches.

In this introductory course, we will provide a high-level overview of Retrieval Augmented Generation and how it improves Generative AI (GenAI).

This course teaches AI practitioners to optimize and deploy large language models using NVIDIA Inference Microservices.

The course focuses on teaching production-level deployment of LLM applications especially enterprise-grade deployment of RAG pipelines.

Learn techniques that can take your RAG system from an interesting proof-of-concept to a serious asset.

Learn how NIM enables the building, deploying, and scaling of AI applications.

Learn how to build a variety of LLM-based applications through the use of modern prompt engineering techniques.

Just like how humans have multiple senses to perceive the world around them, computers have a variety of sensors to help perceive the human world.

Get started quickly in developing LLM-based applications by exploring the open-sourced ecosystem including pretrained LLMs.

Retrieval-Augmented Generation (RAG) pipelines are revolutionizing enterprise operations.

Learn how to write, compile, and run GPU-accelerated code, leverage CUDA core libraries to harness the power of massive parallelism provided by modern GPU accelerators, optimize memory migration between CPU and GPU, and implement your own algorithms.

Bridge the gap between simulation and real-world robotics using ROS 2, Isaac Sim, and NVIDIA Jetson with HIL.

Learn how to build a variety of LLM-based applications through the use of modern prompt engineering techniques.