Step1

Select your Software

Step2

     Choose a GPU      

Step3

 Get Up and Running

Select your Software

Find the software that’s right for you. Choose from an interactive app, customizable frameworks, or high-performance libraries.

NVIDIA DIGITS™

Interactively manage data and train deep learning models for image classification without the need to write code.

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Deep Learning Frameworks

Design and train deep learning models using a high-level interface. Choose a deep learning framework that best suits your needs based on your choice of programming language, platform, and target application.

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NVIDIA Deep Learning SDK

This SDK delivers high- performance multi-GPU acceleration and industry-vetted deep learning algorithms, and is designed for easy drop-in acceleration for deep learning frameworks.

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Choose a GPU

NVIDIA GPUs are available in desktops, notebooks, servers, and supercomputers around the world, as well as in cloud services from Amazon, IBM, and Microsoft. You can choose a “plug-and-play” deep learning solution powered by NVIDIA GPUs or build your own.

Desktop Development

• NVIDIA Titan X – The fastest accelerator for deep neural network training on a desktop PC based on the revolutionary NVIDIA Pascal™ architecture

Data Center Solutions

• NVIDIA DGX-1™ – The world’s first purpose-built system for deep learning with fully integrated hardware and software that can be deployed quickly and easily
• NVIDIA Tesla®P100 – The most advanced accelerator for deep learning training based on the NVIDIA Pascal™ architecture.
• Tesla®P40 – The fastest deep learning inferencing accelerator
• Tesla®P4 – A low-power, small form-factor GPU accelerator optimized for video transcoding, image processing, and deep learning inference

GPU-accelerated Cloud Services

• Amazon Web Services G2 GPU instance
• IBM Softlayer GPU Servers
• Microsoft Azure

Embedded Applications

• Jetson TX1 Developer Kit – a full-featured development platform for visual computing embedded applications
• DRIVE™ PX – a self driving car computer based on the NVIDIA® Tegra® X1 processor

Get Up and Running

Choose the options that best suit your needs and learning style:

Deep Learning Classes and Courses

• NVIDIA Deep Learning Institute offers self-paced training and instructor-led workshops
• CS229: Machine Learning by Andrew Ng (Baidu)
• Deep Learning at Oxford by Nando de Freitas (University of Oxford)
• Neural Networks for Machine Learning by Geoffrey Hinton (Google, University of Toronto)
• Deep Learning for Computer Vision by Rob Fergus (Facebook, NYU)
• Learning From Data by Yasser Abu-Mostafa (Caltech)
• Deep Learning for Natural Language Processing (Stanford)

Blog posts and articles

• Deep Learning in a Nutshell Series by Tim Dettmers (University of Lugano, Switzerland)
• Hacker’s Guide to Neural Networks by Andrej Karpathy (Stanford University)
• Getting Started with DIGITS by Allison Gray (NVIDIA)
• Deep Learning Posts on the ParallelForAll technical blog

Technical Presentation and Webinars

• Getting Started with DIGITS: Deep GPU Training System by Allison Gray (NVIDIA)
• Large-Scale Deep Learning For Building Intelligent Computer Systems by Jeff Dean (Google)
• Deep Learning: What’s Next by Andrew Ng (Baidu)
• Deep Learning at Scale by Ren Wu (Baidu)
• GPUs and Machine Learning: A Look at cuDNN by Sharan Chetlur (NVIDIA)