DataBloomWe love PC games. The newest titles and the greatest classics. FPS, RPG, grand strategy, squad-based tactics, single-player, multiplayer, MMO — you name it, we love it. There are more than 800 games on GeForce NOW — including 80 of the biggest free-to-play games — streaming straight from the cloud. And thanks to the explosive Read article >
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Hey! Sorry, if this question does not make 100% sense as my
education has not yet reached formal ML classes, but I’ll ask
nonetheless.
I want to make a GAN in tensorflow, but instead of just copy and
pasting someone’s code, I want to truly understand the bits and
parts of it.
From what I know about Naive Bayes, it predicts the distribution
of our original data – but after each iteration how can one sample
from this distribution, and additionally once you take a sample
from this distribution, how can we actually in code pass it to our
discriminator?
Thanks everyone 🙂
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Image segmentation and recommender system Jupyter notebooks are now available in the NGC catalog. These Jupyter notebooks come with complete instructions on how to train these models using the resources from the NGC catalog.
Image segmentation and recommender system Jupyter notebooks are now available in the NGC catalog. These Jupyter notebooks come with complete instructions on how to train these models using the resources from the NGC catalog.
Upcoming Webinars
The NVIDIA NGC team is hosting two webinars with live Q&A to dive into two new Jupyter notebooks available from the NGC catalog. Learn how to use these resources to kickstart your AI journey.
NVIDIA NGC Jupyter Notebook Day: Image Segmentation
February 18 at 9 a.m. PT
Image segmentation deals with placing each pixel of an image into specific classes that share common characteristics.
In this session, you’ll learn:
NVIDIA NGC Jupyter Notebook Day: Recommender System
February 18 at 11 a.m. PT
Recommender systems deal with predicting user preferences for products based on historical behavior or actions and are widely used in online retail, social media, streaming video, music platforms, and more.
In this session, you’ll learn:
Hi /r/tensorflow
readers!
We have created a labelling
tool that can be customized to display all sorts of data models
and tasks. Here are a couple of examples for NLP
and CV.
I hope some of you will find this useful, and if you have any
thoughts I would love to hear your feedback!
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Hi, everyone.
I am attempting to constrain some outputs of my regression
network, say x, y, z = model(data), where x, y, z are scalars. The
constrain that I want to impose is that when predicting all three
dependent variables, the condition “x + y <=1.0” must be
honored. Given this description, can I implement this in a forward
function?
Thank you!
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The project, which runs on a NVIDIA Jetson Nano 2GB Developer Kit, monitors the eyes of the user and voices a prompt when their blink rate is less than the recommended rate of 10 blinks per minute.
Thirteen-year-old Adrit Rao, was awarded the Jetson Project of the Month for his Blink Detection and Reminder (Blinkr). The project, which runs on a NVIDIA Jetson Nano 2GB Developer Kit, monitors the eyes of the user and voices a prompt when their blink rate is less than the recommended rate of 10 blinks per minute.
Several studies have shown that low eye blinking rate, usually triggered by the use of a computer screen, is the leading cause of computer vision syndrome and other related disorders. To address this problem, Adrit created Blinkr with a simple setup of Jetson Nano 2GB Developer Kit, a webcam (or a Raspberry Pi v2 camera), a speaker and a few other basic peripherals.
The camera monitors the face of the user and feeds the frames to the Jetson Nano. To detect blinking, Adrit uses a 68 point facial landmark pre-trained model available in the Dlib open source library. Eyes are detected in each frame and the eye aspect ratio (EAR) is calculated and used to record the number of blinks over time. When the total blinks in a minute is less than the recommended rate, the speaker voices an alarm urging the user to blink more.
Many of us working from home do not have the usual prompts or interruptions during our day to move away from our screens. Tools like Blinkr can help us adopt healthy screen habits. This is a great project to build at home to learn about Jetson and AI, and to protect your eyesight.
This project earned Adrit his Jetson AI Specialist certificate. We are keeping our appreciative (and healthy) eyes peeled out to see what he builds next. If you’re interested in building your own Blinkr, he has shared the instructions and the code here.
NVIDIA Omniverse is bringing the new standard in real-time graphics for developers. Check out some of the resources on the NVIDIA On-Demand catalog to learn more tips and tricks for developing in Omniverse.
NVIDIA Omniverse is bringing the new standard in real-time graphics for developers. Teams across industries are now using the open, cloud-native platform to deliver new levels of virtual collaboration and photorealistic simulation to their projects. And with open beta availability recently announced, more developers around the world can experience Omniverse and explore ways to integrate technologies or connect applications.
Check out some of the resources on the NVIDIA On-Demand catalog to learn more tips and tricks for developing in Omniverse:
Getting Started with Omniverse Launcher: Learn more about the Omniverse Launcher as this session covers installation and configuration, as well as an overview of how to install applications and connectors.
What Makes USD Unique: USD is the backbone of the Omniverse collaboration technology; in this video we discuss Pixar’s USD file format, explains the basics of its structure, and introduces layers, references and sublayers.
Deep Dive into Omniverse Kit: Get an introduction to Omniverse Kit and learn how developers can leverage this powerful toolkit to create new Omniverse Apps and extensions.
Download Omniverse today and check out other Omniverse sessions on the NVIDIA On-Demand portal.
Designers, engineers, researchers, creative professionals all need the flexibility to run complex workflows – no matter where they’re working from. With the newest release of NVIDIA virtual GPU (vGPU) technology, enterprises can provide their employees with more power and flexibility through GPU-accelerated virtual machines from the data center or cloud. Available now, the latest version Read article >
The post NVIDIA Expands vGPU Software to Accelerate Workstations, AI Compute Workloads appeared first on The Official NVIDIA Blog.