DataBloomHi
I’m not sure what’s going on with my classification model, as it predicts objects as floats instead of classes. Is it something to do with the loss function or activation functions at the end of the network?
The barebones code can be found here:
submitted by /u/dahkneela
[visit reddit] [comments]
If AI, data science, graphics or robotics is your jam, stream the NVIDIA AI Essentials Learning Series this summer. These intro-level courses provide foundational knowledge to students and early-career developers looking to broaden their areas of expertise. The free series includes over a dozen sessions — each less than an hour long — on topics Read article >
The post Find Your Groove: Add NVIDIA AI Essentials Series to Your Summer Playlist appeared first on The Official NVIDIA Blog.
I want to create a model that is made up by a bunch of objects. Each one has a name and 6 attributes associated with it. I want to make an unsupervised model that groups objects with similar attributes together. When a piece of data is added, I would like to have the group it best fits into outputted and be able to get the names of other objects in this group. Is this possible with tensorflow?
submitted by /u/UnreadyDog
[visit reddit] [comments]
Hi all, I’m trying to use TF-Slim (yes it has to be tf-slim) and I’m having some trouble figuring out how to break my data up into batches. I want to avoid loading my dataset into RAM (although I could), but the documentation doesn’t specify how to handle batches. If anyone that has used tf-slim before could shed some light, it would be much appreciated.
submitted by /u/prinse4515
[visit reddit] [comments]
The NVIDIA Deep Learning Institute (DLI) is offering instructor-led, hands-on training on how to implement multiple AI-based approaches to solve a specific use case of identifying network intrusions for telecommunications.
Whether you need to monitor cybersecurity threats, fraudulent financial transactions, product defects, or equipment health, artificial intelligence can help you catch data abnormalities before they impact your business. AI models can be trained and deployed to automatically analyze datasets, define “normal behavior,” and identify breaches in patterns quickly and effectively. These models can then be used to predict future anomalies. With massive amounts of data available across industries and subtle distinctions between normal and abnormal patterns, it’s critical that organizations use AI to quickly detect anomalies that pose a threat.
The NVIDIA Deep Learning Institute (DLI) is offering instructor-led, hands-on training on how to implement multiple AI-based approaches to solve a specific use case of identifying network intrusions for telecommunications. You’ll learn three different anomaly detection techniques using GPU-accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs) and then implement and compare supervised and unsupervised learning techniques. At the end of the workshop, you’ll be able to use AI to detect anomalies in your work across telecommunications, cybersecurity, finance, manufacturing, and other key industries.
By participating in this workshop, you’ll:
This training will be offered:
Tue, Sep 21, 2021, 9:00 a.m. – 5:00 p.m. CEST/EMEA, UTC+2
Tue, Sep 21, 2021, 9:00 a.m. – 5:00 p.m. PDT, UTC-7
Space is limited, register now.
Join the NGC team for a webinar and live Q&A on Aug. 25, at 10 a.m. PT
The NGC team is hosting a webinar and live Q&A. Topics include how to use containers from the NGC catalog deployed from Google Cloud Marketplace to GKE, a managed Kubernetes service on Google Cloud, that easily builds, deploys, and runs AI solutions.
Building a Computer Vision Service Using NVIDIA NGC and Google Cloud
August 25 at 10 a.m. PT
Organizations are using computer vision to improve the product experience, increase production, and drive operational efficiencies. But, building a solution requires large amounts of labeled data, the software and hardware infrastructure to train AI models, and the tools to run real-time inference that will scale with demand.
With one click, NGC containers for AI can be deployed from Google Cloud Marketplace to GKE. This managed Kubernetes service on Google Cloud, makes it easy for enterprises to build, deploy, and run their AI solutions.
By joining this webinar, you will learn:
There are more AI startups in healthcare than any other single industry. The number of AI startups in media and entertainment is about the same as that in retail. More than one in 10 of all AI startups is based in California. How do we know this? NVIDIA Inception, our acceleration platform for AI startups, Read article >
The post Better Than 8K Resolution: NVIDIA Inception Displays Global AI Startup Ecosystem appeared first on The Official NVIDIA Blog.
NVIDIA today announced the North American availability of NVIDIA Base Command™ Platform, a hosted AI development hub that provides enterprises with instant access to powerful computing infrastructure wherever their data resides.
How are the checkpoint_max_to_keep and checkpoint_every_n options configured in the pipeline configuration files for the object detection api?
I am referring to the parameters to the train_loop function in model_lib_v2.py
Is it as simple as adding these lines as an outermost group in my .config ?
train_loop { checkpoint_max_to_keep=50 checkpoint_every_n=500 }
submitted by /u/Meriipu
[visit reddit] [comments]
Hi! A newbie Here. I am working on a Gesture recognition project with mi band and I need to collect the sensor data from the band. Is there any way to get realtime data from the accelerometer of the band?
Notify App for Mi Band does track realtime data from the accelerometer but is there a way to get that realtime data on the pc for gesture recognition?
Any advice would on this would be really appreciated.
submitted by /u/infinitypenetrator
[visit reddit] [comments]