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menu search help Help Help Center Email support Chat support language Deutsch English español (Latinoamérica) français bahasa Indonesia Italiano 日本語 한국어 Polski português (Brasil) Türkçe 简体中文 繁體中文 Join Sign in close home Home school Catalog home Home school Catalog help Help Privacy Terms Become a cloud expert with hands-on training. We give you temporary credentials to Google Cloud Platform and Amazon Web Services, so you can learn the cloud using the real thing – no simulations. From 30-minute individual labs to multi-day courses, from introductory level to expert, instructor-led or self-paced, with topics like machine learning, security, infrastructure, app dev, and more, we've got you covered. What's Hot Popular Lab Object Detection with DIGITS Many problems have established deep learning solutions, but sometimes the problem that you want to solve does not. Learn to create custom solutions through the challenge of detecting whale faces from aerial images by: Combining traditional computer vision with deep learning Performing minor “brain surgery” on an existing neural network using the deep learning framework Caffe Harnessing the knowledge of the deep learning community by identifying and using a purpose built network and end-to-end labeled data. Upon completion of this lab, you will be able to solve custom problems with deep learning. Introductory Popular Lab Image Classification with DIGITS Deep learning enables entirely new solutions by replacing hand-coded instructions with models learned from examples. Train a deep neural network to recognize handwritten digits by: Loading image data to a training environment Choosing and training a network Testing with new data and iterating to improve performance On completion of this Lab, you will be able to assess what data you should be training from. Introductory Popular Lab Neural Network Deployment with DIGITS and TensorRT Deep learning allows us to map inputs to outputs that are extremely computationally intense. Learn to deploy deep learning to applications that recognize images and detect pedestrians in real time by: Accessing and understanding the files that make up a trained model Building from each function’s unique input and output Optimizing the most computationally intense parts of your application for different performance metrics like throughput and latency Upon completion of this Lab, you will be able to implement deep learning to solve problems in the real world. Introductory Popular Lab Image Segmentation with TensorFlow There are a variety of important applications that need to go beyond detecting individual objects within an image, and that instead need to segment the image into spatial regions of interest. An example of image segmentation involves medical imagery analysis, where it is often important to separate the pixels corresponding to different types of tissue, blood or abnormal cells, so that you can isolate a particular organ. Another example includes self-driving cars, where segmenting an image into distinct areas is needed to understand road scenes. In this lab, you will learn how to train and evaluate an image segmentation network using TensorFlow. Intermediate Trending Lab Deployment for Intelligent Video Analytics using TensorRT First version Advanced Trending Lab Image Classification with Microsoft Cognitive Toolkit 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. Introductory Trending Lab Managing Accelerated Application Memory with CUDA C/C++ Unified Memory and nvprof Leverage the NVIDIA Command-Line Profiler and an understanding of Unified Memory to iteratively optimize CUDA C/++ accelerated applications. Fundamental Highly Rated Lab GPU Memory Optimizations with C/C++ 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++. Expert Highly Rated Lab Neural Network Deployment with DIGITS and TensorRT Deep learning allows us to map inputs to outputs that are extremely computationally intense. Learn to deploy deep learning to applications that recognize images and detect pedestrians in real time by: Accessing and understanding the files that make up a trained model Building from each function’s unique input and output Optimizing the most computationally intense parts of your application for different performance metrics like throughput and latency Upon completion of this Lab, you will be able to implement deep learning to solve problems in the real world. Introductory Highly Rated Lab Accelerating Applications with GPU-Accelerated Libraries in C/C++ Learn how to accelerate your C/C++ application using drop-in libraries to harness the massively parallel power of NVIDIA GPUs. In about two hours, you will work through three exercises, including: Use cuBLAS to accelerate a basic matrix multiply Combine libraries by adding some cuRAND API calls to the previous cuBLAS calls Use nvprof to profile code and optimize with some CUDA Runtime API calls Advanced Become a Cloud Expert Infrastructure & DevOps Implement, deploy, migrate and maintain applications in the cloud. Websites & App Dev For software engineers who develop applications in the cloud. Big Data Design, build, analyze, and optimize big data solutions. Machine Learning Write distributed machine learning models that scale. Security, Backup & Recovery Stay compliant and protect information, data applications and infrastructure. Search the entire catalog Privacy Terms...

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