Computer Vision and Machine Learning Interest Group

Computer Vision and Machine Learning Interest Group

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Group for people interested in Computer Vision, Machine Learning, Image Processing and Deep Learning. Share your ideas, thoughts, doubts and solutions.

BMW InnovationLab 16/12/2019

BMW shares AI algorithms used in production, available on GitHub

Github:

BMW InnovationLab This organization contains open source software published by the developers and partners of the BMW InnovationLab - BMW InnovationLab

University of Toronto researchers develop AI that can defeat facial recognition systems 02/06/2018

https://venturebeat.com/2018/05/31/university-of-toronto-researchers-develop-ai-that-can-defeat-facial-recognition-systems/

University of Toronto researchers develop AI that can defeat facial recognition systems Researchers at the University of Toronto have developed an adversarial neural network that can defeat facial recognition systems.

A Friendly Introduction to Convolutional Neural Networks | Deep_In_Depth : Data Science and Deep Learning 07/09/2017

http://www.scoop.it/t/data-science-58/p/4083777860/2017/08/28/a-friendly-introduction-to-convolutional-neural-networks?utm_content=bufferf32f6&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer

A Friendly Introduction to Convolutional Neural Networks | Deep_In_Depth : Data Science and Deep Learning Convolutional neural networks (or convnets for short) are used in situations where data can be expressed as a "map" wherein the proximity between two data points indicates how related they are. An image is such a map, which is why you so often hear of convnets in the context of image analysis. If...

How powerful are Graph Convolutional Networks? 05/08/2017

http://tkipf.github.io/graph-convolutional-networks/ -part-iii-embedding-the-karate-club-network

How powerful are Graph Convolutional Networks? Many important real-world datasets come in the form of graphs or networks: social networks, knowledge graphs, protein-interaction networks, the World Wide Web, etc. (just to name a few). Yet, until recently, very little attention has been devoted to the generalization of neural...

Essential Cheat Sheets for Machine Learning and Deep Learning Engineers 06/06/2017

https://medium.com//essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5

Essential Cheat Sheets for Machine Learning and Deep Learning Engineers Learning machine learning and deep learning is difficult for newbies. As well as deep learning libraries are difficult to understand. I am…

Keras Tensorflow tutorial: Practical guide from getting started to developing complex deep neural network 20/05/2017

http://cv-tricks.com/tensorflow-tutorial/keras/

Keras Tensorflow tutorial: Practical guide from getting started to developing complex deep neural network In this Keras Tensorflow tutorial, learn to install Keras, understand Sequential model & functional API to build VGG and SqeezeNet networks with example code

Facebook open-sources Caffe2, a new deep learning framework 19/04/2017

https://venturebeat.com/2017/04/18/facebook-open-sources-caffe2-a-new-deep-learning-framework/

Facebook open-sources Caffe2, a new deep learning framework At its F8 developer conference in San Jose today, Facebook is announcing the launch of Caffe2, a new open-source framework for deep learning, a trendy type of artificial intelligence (AI). Deep lea…

Bias-Variance Tradeoff in Machine Learning | Learn OpenCV 21/03/2017

Bias-Variance Tradeoff in Machine Learning

http://www.learnopencv.com/bias-variance-tradeoff-in-machine-learning/

Bias-Variance Tradeoff in Machine Learning | Learn OpenCV In this post, we explain the bias-variance tradeoff in machine learning and discuss ways to minimize errors. We also discuss the problem of model selection.

google/butteraugli 21/03/2017

Interested image quality metric from Google

https://github.com/google/butteraugli

google/butteraugli butteraugli estimates the psychovisual difference between two images

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