Prerequisites: Proficiency programming in C/C++ and professional experience working on HPC applications.
Data Augmentation and Segmentation with Generative Networks for Medical Imaging
From virtual desktops, applications, and workstations to optimized containers in the cloud, find everything you need to get started with deskside deep learning—right where you want it.You can quickly and easily access all the software you need for deep learning training from NGC.
With DLI, you’ll have access to a fully configured, GPU-accelerated server in the cloud, gain practical skills for your work, and have the opportunity to earn a certificate of subject matter competency.Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities.Explore how to build a deep learning classification project with computer vision models using an NVIDIALearn how to optimize TensorFlow models to generate fast inference engines in the deployment stage.Learn how to scale deep learning training to multiple GPUs with Horovod, the open-source distributed training framework originally built by Uber and hosted by the LF AI Foundation.Explore how to classify and forecast time-series data, such as modeling a patient's health over time, using recurrent neural networks (RNNs).Explore an introduction to deep learning for radiology and medical imaging by applying CNNs to classify images in a medical imaging dataset.Learn how to apply deep learning techniques to detect the 1p19q co-deletion biomarker from MRI imaging.Learn how to use Coarse-to-Fine Context Memory (CFCM) to improve traditional architectures for medical image segmentation and classification tasks.Learn how to use generative adversarial networks (GANs) for medical imaging by applying them to the creation and segmentation of brain MRIs.Learn how to build hardware-accelerated applications for intelligent video analytics (IVA) with DeepStream and deploy them at scale to transform video streams into insights.Learn how to build DeepStream applications to annotate video streams using object detection and classification networks.Learn how to accelerate and optimize existing C/C++ CPU-only applications to leverage the power of GPUs using the most essential CUDA techniques and the Nsight Systems profiler.Explore how to use Numba—the just-in-time, type-specializing Python function compiler—to create and launch CUDA kernels to accelerate Python programs on GPUs.Explore how to build and optimize accelerated heterogeneous applications on multiple GPU clusters using OpenACC, a high-level GPU programming language.Learn how to reduce complexity and improve portability and efficiency of your code by using a containerized environment for high-performance computing (HPC) application development.Learn how to accelerate C/C++ or Fortran applications using OpenACC to harness the power of GPUs.Learn how to perform multiple analysis tasks on large datasets using RAPIDS, a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows.Learn to build a GPU-accelerated, end-to-end data science workflow using RAPIDS open-source libraries for massive performance gains.Explore an introduction to AI, GPU computing, NVIDIA AI software architecture, and how to implement and scale AI workloads in the data center.
Fundamentals of Accelerated Computing with CUDA C/C++
Live classes will be delivered through the Scientific Programming School, which is an interactive and advanced e-learning platform for learning scientific coding.Students purchasing this course will receive free access to the interactive version (with Scientific code playgrounds) of this course from the CUDA provides a general-purpose programming model which gives you access to the tremendous computational power of modern GPUs, as well as powerful libraries for machine learning, image processing, linear algebra, and parallel algorithms.Some of the images used in this course are copyrighted to NVIDIA.