submitted by /u/ApproximateIdentity
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Category: Misc
Hello all! I have spent some time working on my chatbot and it’s
working pretty well. I have a json file that stores all my intents,
but I have come across a problem that I don’t know how to solve. I
want to have an “other” tag. This tag should be called whenever the
input doesn’t match any other patterns or tags. The goal of this is
so that if no tags are matched, I have a separate set of
instructions for my program to follow in such cases. Does anyone
have any idea how I can go about this? Is there a certain pattern I
should have or what? Also, another question I have is what if a
certain pattern I have has variables in it, for example, “Play
Clocks by Coldplay”. In the case of “Play {songName} by {artist}” a
constant pattern cannot be used since the user can come up with any
combination of song names and artists. Any help is appreciated.
Thank you in advance!
submitted by /u/Rafhay101
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NVIDIA DRIVE IX is an open, scalable cockpit software platform that provides AI functions to enable a full range of in-cabin experiences, including intelligent visualization with augmented reality and virtual reality, conversational AI and interior sensing.
The post A Trusted Companion: AI Software Keeps Drivers Safe and Focused on the Road Ahead appeared first on The Official NVIDIA Blog.
To help data scientists and developers simplify their AI workflows, we have collaborated with Amazon Web Services (AWS) to bring NVIDIA NGC software resources directly to the AWS Marketplace.
Enterprises across industries are adopting AI to drive business growth and they’re relying on cloud infrastructure to develop and deploy their solutions.
To help data scientists and developers simplify their AI workflows, we have collaborated with Amazon Web Services (AWS) to bring NVIDIA NGC software resources directly to the AWS Marketplace. The AWS Marketplace is where customers find, buy and immediately start using software and services that run on AWS.
The NVIDIA NGC catalog provides GPU-optimized AI software for data engineers, data scientists, developers, and DevOps teams so they can focus on building and deploying their AI solutions faster.
More than 250,000 unique users have now downloaded over 1 million of the AI containers, pretrained models, application frameworks, Helm charts and other machine learning resources available on the NGC catalog.
Available free of charge, the software from the NGC catalog is optimized to run on NVIDIA GPU cloud instances, such as the Amazon EC2 P4d instance featuring the record-breaking performance of NVIDIA A100 Tensor Core GPUs.
Instant Access to Performance-Optimized AI Software
NGC software in AWS Marketplace provides a number of benefits to help data scientists and developers build AI solutions.
- Faster software discovery: Through the AWS Marketplace, developers and data scientists can access the latest versions of NVIDIA’s AI software with a single click.
- The latest NVIDIA software: The NGC software in AWS Marketplace is automatically updated to the latest versions as soon as they’re available in the NGC catalog. The software is constantly optimized, and the monthly releases give users access to the latest features and performance improvements.
- Simplified software deployment: Users of Amazon EC2, Amazon SageMaker, Amazon Elastic Kubernetes Service (EKS) and Amazon Elastic Container Service (ECS) can quickly subscribe, pull and run NGC software on NVIDIA GPU instances, all within the AWS console. Additionally, SageMaker users can simplify their workflows by eliminating the need to first store a container in Amazon Elastic Container Registry (ECR).
- Continuous integration and development: NGC Helm charts are also available in AWS Marketplace to help DevOps teams quickly and consistently deploy their services.
Here’s a step-by-step guide to quickly discover the NGC software and run an object detection service on Amazon EC2 instances.
Accelerate your AI development on NVIDIA GPU-powered AWS services today with the NGC catalog in AWS Marketplace.
I am trying to implement a very basic recursive neural network
into my linear regression analysis project in Tensorflow that takes
two inputs passed to it and then a third value of what it
previously calculated. So, my project is trying to calculate
something across the next x number of years, and after the first
year I want it to keep taking the value of the last year.
Currently, my training data has two inputs, not three, predicting
one output, so how could I make it recursive, so it keeps on
passing in the value from the last year, to calculate the next? To
explain slightly further, if it were to calculate across the next 5
years:
1st year:
Input 1: 10
Input 2: 20
(Maybe need input 3, but a value that has no affect on the
linear regression model)
Output: 30
2nd year:
Input 1: 11
Input 2: 22
Input 3: 30 (1st year output)
Output: 35
3rd Year:
Input 1:12
Input 2: 24
Input 3: 35 (2nd year output)
Output: 40
submitted by /u/HexadecimalHero
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I have a problem downloading tensorflow
I have tried to download tensorflow through pip install
tensorflow but only get ERROR: Could not find a version that
satisfies the required tensorflow and ERROR: No matching
distribution found for tensorflow. I have updated pip to 20.3.3 and
I have python 3.9.1. I’m running it in pycharm, cmd, and visual
studio. How can I fix this?
submitted by /u/Creeperhaten1
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I’m trying to create my own model to classify a face as either
wearing a mask or not, and by what ratio. This
is my Colab notebook, with predictions output at the end.
The question is:
How do I make the model predict with confidence, for example:
[0.966 0.034]?
Note: I didn’t use binary_crossentropy with one neuron dense
layer on purpose for this model, as I am planning on adding a 3rd
class (mask worn incorrectley) as soon as I have a better
dataset.
submitted by /u/LGariv
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Arman Toorians isn’t your average classic car restoration hobbyist. The NVIDIA engineer recently transformed a 1974 Triumph TR6 roadster at his home workshop into an EV featuring AI. Toorians built the vehicle to show a classic car can be recycled into an electric ride that taps NVIDIA Jetson AI for safety, security and vehicle management Read article >
The post Electric Avenue: NVIDIA Engineer Revs Up Classic Car to Sport AI appeared first on The Official NVIDIA Blog.
In this interview, Miguel Molina, Director of Developer Relations at SplitmediaLabs, the makers of XSplit, discussed how they were able to easily integrate NVIDIA Broadcast into their vastly popular streaming service.
In this interview, Miguel Molina, Director of Developer Relations at SplitmediaLabs, the makers of XSplit, discussed how they were able to easily integrate NVIDIA Broadcast into their vastly popular streaming service.
For those who may not know, tell us about yourself?
My name is Miguel Molina, currently the Director of Developer Relations at SplitmediaLabs, the makers of XSplit. I’ve been with the company since before its inception, starting out as a software engineer, moving onto product management, and finally landing in business development where I work with our industry partners to find integrations and opportunities that bring value to our customers.
Tell us about Xsplit and the success of the company thus far.
XSplit is the brand that got us to where we are now and XSplit Broadcaster is the hero product behind it all. It’s a simple yet powerful live streaming and recording software for producing and delivering rich video content that powers countless live streams and recordings around the world.
What excited you most about NVIDIA Broadcast Engine?
Being able to add value to our products is a priority for us and the NVIDIA Broadcast Engine gives us just that in a straightforward package. With features that improve video, audio, and augmented reality, the SDK has the potential to massively improve the output of different types of media, vastly improving the user experience for various use cases.
Why were you interested in integrating the Audio Effects SDK?
We were looking for an alternative to CPU-based background noise removal and NVIDIA’s demo videos showing off NVIDIA’s noise removal feature got us sold on the idea. After receiving a sample, we decided to commit to integrating it into XSplit Broadcaster.
How was the experience integrating the SDK?
It was as simple as looking at the sample code, putting the relevant code segments in their proper places, and hitting compile. The initial integration itself just took a few hours and a working build was available the same day we started on it.
Any surprises or unexpected challenges?
We were initially having massive CUDA utilization in an early alpha build of the SDK but NVIDIA engineers were very responsive and quickly isolated the issue on their end and were able to provide an updated build that fixed the problem.
How have your users responded to the improved experience?
Our users love the fact that they are able to utilize NVIDIA’s noise removal natively within XSplit Broadcaster. It’s as simple as turning it on and it just works.
What new features or SDKs from NVIDIA are you looking forward to now?
We are looking to update our NVIDIA Video Codec SDK implementation so we can provide better granular preset control over quality versus performance on NVENC.
Which of the NBX SDKs are you most interested in beyond Audio?
Definitely the Video Effects SDK as their Virtual Background and Super Resolution features would be quite useful with people mostly staying at home these days.
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Developers can download XSplit Broadcaster here.
To learn more about NVIDIA Broadcast, or to get started, visit our page here.