[MobiNetV1] Removing people from complex backgrounds in real time using TensorFlow.js in the web browser!

This code attempts to learn over time the makeup of the background of a video such that the algorithm can attempt to remove any humans from the scene. This is all happening in real time, in the browser, using TensorFlow.js.


https://lnkd.in/gsePqBH

#deeplearning #machinelearning #artificialintelligence

❇️ @AI_Python_EN

Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner.

abs: https://arxiv.org/abs/2002.05534v1

#rnn #machinelearning #ArtificialIntelligence #DeepLearning #

❇️ @AI_Python_EN

Introduction to Reinforcement Learning

By DeepMind : https://lnkd.in/dd2VNhH

#ArtificialIntelligence #DeepLearning #ReinforcementLearning

Access 2 new free online courses
as of today on edXOnline
It's time to hone your #digitalintelligence knowledge and skills, even more if you're getting bored at home:
http://bit.ly/2WLF58R

#DeepLearning

❇️ @AI_Python_EN

Contributor Derrick Mwiti with an overview of #TensorFlow MLIR—a mult-level intermediate representation designed to be a reusable and extensible compiler that works across the #DeepLearning landscape.
https://bit.ly/2Jt83T7

❇️ @AI_Python_EN

Breast cancer classification with Keras and Deep Learning

To analyze the cellular structures in the breast histology images we were instead leveraging basic computer vision and image processing algorithms, but combining them in a novel way.

Researcher: Adrian Rosebrock
Paper & codes : http://ow.ly/yngq30qjLye

#artificialintelligence #ai #machinelearning #deeplearning #bigdata #datascience

❇️ @AI_Python_EN

Acme: A new framework for distributed reinforcement learning by DeepMind
Intro:
https://deepmind.com/research/publications/Acme
Paper:
https://github.com/deepmind/acme/blob/master/paper.pdf
Repo:
https://github.com/deepmind/acme

#reinforcementlearning #ai #deepmind #deeplearning #machinelearning

🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python

#TFHub tutorials TensorFlow Hub is a library for reusable machine learning modules : https://lnkd.in/eYk83J5 #DeepLearning #Tensorflow #TensorFlowHub #Tutorials

Big GANs Are Watching

You It is the state-of-the-art unsupervised GAN, which parameters are publicly available. They demonstrate that object saliency masks for GAN-produced images can be obtained automatically with BigBiGAN. These masks then are used to train a discriminative segmentation model. Being very simple and easy-to-reproduce, our approach provides competitive performance on common benchmarks in the unsupervised scenario.

Github: https://github.com/anvoynov/BigGANsAreWatching

Paper : https://arxiv.org/abs/2006.04988

#datascience #machinelearning #artificialintelligence #deeplearning

Google Research • Representation Learning for Information Extraction from Templatic Documents such as receipts, bills, insurance quotes. We propose a novel approach using representation learning for tackling the problem of extracting structured information from form-like document images.

Blogpost

https://ai.googleblog.com/2020/06/extracting-structured-data-from.html?m=1

Paper

https://research.google/pubs/pub49122/
We propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates, and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. These learned representations are not only useful in solving the extraction task for unseen document templates from two different domains, but are also interpretable, as we show using loss cases. #machinelearning #deeplearning #datascience #dataengineer #nlp

Google Research • Representation Learning for Information Extraction from Templatic Documents such as receipts, bills, insurance quotes. We propose a novel approach using representation learning for tackling the problem of extracting structured information from form-like document images.

Blogpost

https://ai.googleblog.com/2020/06/extracting-structured-data-from.html?m=1

Paper

https://research.google/pubs/pub49122/
We propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates, and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. These learned representations are not only useful in solving the extraction task for unseen document templates from two different domains, but are also interpretable, as we show using loss cases. #machinelearning #deeplearning #datascience #dataengineer #nlp

Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules Mittal et al.: #ArtificialIntelligence #DeepLearning #MachineLearning

https://arxiv.org/abs/2006.16981

Stanford CS224w’s lectures Machine Learning with Graphs, Leskovec et al.: https://lnkd.in/d4Cnahj #DeepLearning #Graphs #MachineLearning