Category: Misc

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Misc

TensorFlow Lite or hardware acceleration sdk for mobile (like Arm NN SDK)

Hello,

I’m trying to develop an android application that uses a deep learning algorithm on the mobile itself, i.e. locally.

I want to make it run on GPU, and from what I understand there are actually TFLite delegates that can help me achieve that. However, after a little research I found out that I can also use the Arm NN SDK with TFLite. Will the use of Arm NN actually make the application run faster, or is TFLite by itself a good choice?

Thanks in advance!

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Interesting use case using TensorFlow JS and custom hardware powered by machine learning.

Interesting use case using TensorFlow JS and custom hardware powered by machine learning. submitted by /u/nbortolotti
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First time here: Learning Tensorflow with Machine Learning

Hi, this is my first time posting in this group so I apologize in advance if this is the wrong place.

I recently picked up the Deep Learning with Python book by Chollet as recommended by Google when first starting to learn Tensorflow. However, I thought a good place to start would be to learn how to code basic ML models like Linear Regression, Support Vector Machines, Random Forests, and basic methods like nested cross validation, preprocessing, grid search. These are all methods I know how to do in sklearn, but want to know in Tensorflow. What book/place would do a good job of explaining the code and intuition behind those models?

I am not looking for what the models mean or how they work, more so how to write them properly in Tensorflow. I took a look at Tensorflow’s tutorial on Linear Regression and it made little sense creating a keras.Sequential model with a Dense layer. Seems similar to a Neural Network. I figure that confusion is because I do not know the reason behind the code.

Thank you in advance.

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Long-Term Stock Forecasting

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Forecasting Dividends

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Stream a Little Stream: GeForce NOW Brings New Interactive Experience to Life

As the Sundance Film Festival kicks off today, “Baymax Dreams of Fred’s Glitch,” an interactive short from Disney Media & Entertainment Distribution and Disney Television Animation, is streaming from the cloud to virtual festivalgoers, using GeForce NOW. The interactive short is part of the New Frontier Alliance Showcase at the 2021 Sundance Film Festival, a Read article >

The post Stream a Little Stream: GeForce NOW Brings New Interactive Experience to Life appeared first on The Official NVIDIA Blog.

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Latest from KDnuggets: Find code implementation for any AI/ML paper using this new chrome extension!

submitted by /u/MLtinkerer

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TF based animation


TF based animation		 submitted by /u/BlakeYerian

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Developer Blog: Scaling Out the Deep Learning Cloud Efficiently

NVIDIA has partnered with One Convergence to solve the problems associated with efficiently scaling on-premises or bare metal cloud deep learning systems.

NVIDIA has partnered with One Convergence to solve the problems associated with efficiently scaling on-premises or bare metal cloud deep learning systems.

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Misc

Upcoming Webinar: Building AI-Based Simulations with NVIDIA SimNet

Join this webinar to learn how NVIDIA SimNet addresses a wide range of use cases involving coupled forward simulations without any training data, as well as inverse and data assimilation problems.

Simulations are prevalent in science and engineering fields and have been recently advanced by physics-driven AI.

Join this webinar to learn how NVIDIA SimNet addresses a wide range of use cases involving coupled forward simulations without any training data, as well as inverse and data assimilation problems.

SimNet is integrated with parameterized constructive solid geometry as well as STL modules to generate point clouds and researchers can customize it with APIs to implement new geometry and physics. It also has advanced network architectures that are optimized for high-performance GPU computing and offers scalable performance for multi-GPU and multi-node implementations with accelerated linear algebra.

By attending this webinar, you’ll learn about:

  • Neural network solver methodology and the SimNet architecture
  • Real-world use cases, from challenging forward multi-physics simulations with turbulence and complex 3D geometries to industrial design optimization and inverse problems 
  • User implementation of two-phase flow in a porous media in SimNet
  • SimNet results and what’s next for the toolkit 

Register now