Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities. In this hands-on course, you will learn the basics of deep learning by training and deploying neural networks. You will: ● Implement common deep learning workflows such as image classification and object detection ● Experiment with data, training parameters, network structure, and other strategies to increase performance and capability ● Deploy your networks to start solving real world problems On completion, you will be able to solve your own problems with deep learning. The Quest below is an older version of this course. For the newer more up-to-date version of this course please explore the new DLI Cloud Platfrom here: https://courses.nvidia.com/courses/course-v1:DLI+C-FX-01+V2/about
In this workshop titled Deep Learning for Digital Content Creation, attendees will receive hands-on training on the latest techniques for designing, training and deploying neural networks for digital content creation. You will learn how to: ●Train a Generative Adversarial Network (GAN) to generate images ● Explore the architectural innovations and training techniques used to make arbitrary video style transfer ● Train your own denoiser for rendered images
In this hands-on course, you will learn how to apply Convolutional Neural Networks (CNNs) to MRI scans to perform a variety of medical tasks and calculations. You will: ● Perform image segmentation on MRI images to determine the location of the left ventricle ● Calculate ejection fractions by measuring differences between diastole and systole using CNNs applied to MRI scans to detect heart disease ● Apply CNNs to MRI scans of LGGs to determine 1p/19q chromosome co-deletion status Upon completion of this course, you’ll be able to apply CNNs to MRI scans to conduct a variety of medical tasks.
In this hands-on course, you will learn the basics of deep learning and how to apply deep learning to detect chromosome co-deletion and search for motifs in genomic sequences. You will: ● Understand the basics of Convolutional Neural Networks (CNNs) and how they work ● Apply CNNs to MRI scans of LGGs to determine 1p/19q chromosome co-deletion status ● Use the DragoNN toolkit to simulate genomic data and to search for motifs Upon completion of this course, you’ll be able to: understand how CNNs work, evaluate MRI images using CNNs, and use real regulatory genomic data to research new motifs.
Accelerate your C/C++ applications on the massively parallel NVIDIA GPUs using CUDA. This course is for anyone with some C/C++ experience who’s interested in accelerating the performance of their applications beyond the limits of CPU-only programming. In this course, you’ll learn how to: • Extend your C/C++ code with the CUDA programming model • Write and launch kernels that execute with massive parallelism on an NVIDIA GPU • Profile and optimize your accelerated programs Upon completion, you’ll be able to write massively parallel heterogeneous programs on powerful NVIDIA GPUs, and optimize their performance by utilizing NVVP.
Learn the basics of OpenACC, a high-level programming language for programming on GPUs. This course is for anyone with some C/C++ experience who is interested in accelerating the performance of their applications beyond the limits of CPU-only programming. In this course, you’ll learn: • Four simple steps to accelerating your already existing application with OpenACC • How to profile and optimize your OpenACC codebase • How to program on multi-GPU systems by combining OpenACC with MPI Upon completion, you’ll be able to build and optimize accelerated heterogeneous applications on multiple GPU clusters using a combination of OpenACC, CUDA-aware MPI, and NVIDIA profiling tools.
Prerequisites: Basic Python competency Duration: 8 hours This hands-on course explores how to use Numba – the just-in-time, type-specializing, Python function compiler – to accelerate Python programs to run on massively parallel NVIDIA GPUs. You’ll learn how to: • Use Numba to compile CUDA kernels from NumPy ufuncs • Use Numba to create and launch custom CUDA kernels • Apply key GPU memory management techniques Upon completion, you’ll be able to use Numba to compile and launch CUDA kernels to accelerate your Python applications on NVIDIA GPUs.
In this lab, you'll learn about a number of memory optimization techniques when programming with CUDA Fortran for an NVIDIA GPU. You'll be working with a basic matrix transpose example. The prerequisites for this lab are as follows: Basic knowledge of programming with CUDA Fortran Please read the instructions at the bottom of this page before clicking the Start Lab button!
This lab teaches you how to use the Computational Network Toolkit (CNTK) from Microsoft for training and testing neural networks to recognize handwritten digits. You will work through a series of examples that will allow you to design, create, train and test a neural network to classify the MNIST handwritten digit dataset, illustrating the use of convolutional, pooling and fully connected layers as well as different types of activation functions. By the end of the lab you will have basic knowledge of convolutional neural networks, which will prepare you to move to more advanced usage of CNTK.
In this lab, you'll learn about a number of memory optimization techniques when programming with CUDA C/C++ for an NVIDIA GPU. You'll be working with a basic matrix transpose example. Users should have basic knowledge of programming with CUDA C/C++.