GTC에서의 실습 교육

Learn the key techniques and tools required to train a deep learning model through hands-on exercises in computer vision and natural language processing.

Learn how to use natural language processing transformer-based models for “text classification” tasks.

Learn the fundamental tools and techniques for running GPU-accelerated Python applications using CUDA GPUs and the Numba compiler.

Get started with gen AI application development. Learn how to build a text-to-image generative AI application using the latest techniques.

Build a deep learning model to automate the verification of capacitors in an NVIDIA printed circuit board (PCB) using a real production dataset. Lower the verification cost and increase the production across various use cases.

Discover the techniques for data-parallel deep learning training on multiple GPUs and work with deep learning tools, frameworks, and workflows to perform neural network training.

Learn how applications powered by large language models can help enterprises automate language-related and many other tasks, helping to streamline operations, decrease expenses, and increase productivity.

Learn how to create an end-to-end hardware-accelerated machine learning pipeline for large datasets by using NVIDIA RAPIDS™ and Dask to scale data science workloads.

Learn about multiple AI-based solutions that solve important telecommunications problems by identifying network intrusions.

Learn how to train the largest neural networks and deploy them to production using techniques to overcome the challenges associated with a large-model memory footprint.

Learn how to use NVIDIA Omniverse™ Replicator, a core Omniverse extension, to accelerate the development of computer vision models.