<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Deeplearning on Den's Hub: Technology Solutions, Guides and Best Practices</title><link>https://denshub.com/en/tags/deeplearning/</link><description>Recent content in Deeplearning on Den's Hub: Technology Solutions, Guides and Best Practices</description><generator>Hugo</generator><language>en</language><copyright>This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.</copyright><lastBuildDate>Thu, 16 Nov 2023 11:30:17 +0200</lastBuildDate><atom:link href="https://denshub.com/en/tags/deeplearning/index.xml" rel="self" type="application/rss+xml"/><item><title>New NVIDIA Jetson Xavier NX Super Module</title><link>https://denshub.com/en/nvidia-jetson-small-ai-computer/</link><pubDate>Sat, 16 May 2020 12:32:39 +0200</pubDate><guid>https://denshub.com/en/nvidia-jetson-small-ai-computer/</guid><description>&lt;p&gt;NVIDIA® Jetson Xavier™ NX brings supercomputer performance to the edge in a small form factor system-on-module (SOM). Up to 21 TOPS of accelerated computing delivers the horsepower to run modern neural networks in parallel and process data from multiple high-resolution sensors — a requirement for full AI systems.&lt;/p&gt;
&lt;h2 id="cloud-native" class="headerLink"&gt;&lt;a href="#cloud-native" class="header-mark" aria-label="Permalink to Cloud Native"&gt;&lt;/a&gt;Cloud Native
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&lt;p&gt;Jetson Xavier NX now features cloud-native support that lets developers build and deploy high-quality, software-defined features on embedded and edge devices. Pre-trained AI models from NVIDIA NGC and the NVIDIA Transfer Learning Toolkit give you a faster path to trained and optimized AI networks, while containerized deployment to Jetson devices allows flexible and seamless updates. Jetson Xavier NX accelerates the NVIDIA software stack with more than 10X the performance of its widely adopted predecessor, Jetson TX2.&lt;/p&gt;</description></item><item><title>Megvii Open Sources Deep Learning Framework</title><link>https://denshub.com/en/megvii-open-source-megengine/</link><pubDate>Sat, 28 Mar 2020 00:15:00 +0100</pubDate><guid>https://denshub.com/en/megvii-open-source-megengine/</guid><description>&lt;p&gt;Chinese Artificial Intelligence (AI) start-up &lt;a href="https://en.megvii.com/" target="_blank" rel="noopener noreferrer"&gt;Megvii Technology Limited&lt;/a&gt; announced that it makes its deep learning framework open-source, as China steps up the development of home-grown AI and makes the technologies more accessible to reduce reliance on US platforms.&lt;/p&gt;</description></item><item><title>Best Courses on TensorFlow and PyTorch</title><link>https://denshub.com/en/tensorflow-pytorch-courses/</link><pubDate>Fri, 31 Jan 2020 10:32:47 +0100</pubDate><guid>https://denshub.com/en/tensorflow-pytorch-courses/</guid><description>&lt;p&gt;This article reviews some of the best online courses for learning TensorFlow and PyTorch, two leading frameworks in the field of deep learning. It highlights key features, course content, and the skills you can expect to gain from each program. Whether you&amp;rsquo;re a beginner or looking to deepen your expertise, these courses provide valuable resources to help you excel in machine learning and artificial intelligence.&lt;/p&gt;
&lt;h2 id="intro" class="headerLink"&gt;&lt;a href="#intro" class="header-mark" aria-label="Permalink to Intro"&gt;&lt;/a&gt;Intro
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&lt;p&gt;TensorFlow and PyTorch are both popular deep learning frameworks, and both have their own strengths and weaknesses. TensorFlow is widely adopted and has a large community, while PyTorch has been growing in popularity and is known for its ease of use and flexibility.&lt;/p&gt;</description></item></channel></rss>