DataBloom
In this three-part series, you discover how to use NVIDIA Nsight Compute for iterative, analysis-driven optimization.
In this three-part series, you discover how to use NVIDIA Nsight Compute for iterative, analysis-driven optimization. Part 1 covers the background and setup needed, part 2 covers beginning the iterative optimization process, and part 3 covers finishing the analysis and optimization process and determining whether you have reached a reasonable stopping point.
In this post, we discuss the various considerations for enabling Tensor Cores in NVIDIA libraries.
In this post, we discuss the various considerations for enabling Tensor Cores in NVIDIA libraries.
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The post New Games, New Features — That’s GFN Thursday appeared first on The Official NVIDIA Blog.
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, 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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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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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.