industryterm:convolutional neural network

A Brief History of Computer Vision (and Convolutional Neural Networks)
▻https://hackernoon.com/a-brief-history-of-computer-vision-and-convolutional-neural-networks-8fe

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Although Computer Vision (CV) has only exploded recently (the breakthrough moment happened in 2012 when AlexNet won ImageNet), it certainly isn’t a new scientific field.Computer scientists around the world have been trying to find ways to make machines extract meaning from visual data for about 60 years now, and the history of Computer Vision, which most people don’t know much about, is deeply fascinating.In this article, I’ll try to shed some light on how modern CV systems, powered primarily by convolutional neural networks, came to be.I’ll start with a work that came out in the late 1950s and has nothing to do with software engineering.One of the most influential papers in Computer Vision was published by two neurophysiologists — David Hubel and Torsten Wiesel — in 1959. Their publication, (...)

#machine-learning #ai #computer-vision #deep-learning #hackernoon-top-story

Introduction to #keras: Build a Neural Network to Classify Digits!
▻https://hackernoon.com/introduction-to-keras-build-a-neural-network-to-classify-digits-89ff3504

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Keras is a neural networks API that runs on top of Tensorflow, Theano, or CNTK. Essentially, Keras provides high level building blocks for developing deep learning models and uses backend engines like Tensorflow to operate. As a “hello world” tutorial to Keras, we will be building a handwritten digit classifier using a convolutional neural network (CNN)!Before getting started you should…Have some python knowledgeUnderstand the basics of neural networksHave the following packages installed:Python 2.7+TensorflowKerasNumpyMatplotlibNumpy is the core python library for scientific computing and Matplotlib is another library for creating data visualizations. If you are unfamiliar with Python and Numpy, I would highly recommend reading through this guide from the popular Stanford CS231 class on (...)

#build-a-neural-network #neural-networks #classify-digits #machine-learning

ConvNet from scratch : just lovely Numpy, Forward Pass |Part 1|
▻https://hackernoon.com/convnet-from-scratch-just-lovely-numpy-forward-pass-part-1-6d3a0776f90a?

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High level frameworks and APIs make it a lot easy for us to implement such a complex architecture but may be implementing them from scratch gives us the ground truth intuition of how actually ConvNets work.- Outline of the ArticleWe’ll be implementing the building blocks of a convolutional neural network! Each function we’ll implement will have detailed instructions that will walk you through the steps needed:Zero-PaddingConvolution forwardPooling forwardWe’ll use DLS jupyter notebooks to execute our modules. Check out DLS here. The fact is it comes with pre-installed libraries and frameworks required for Deep Learning. So it’s good to go for DL.A video walkthrough of Deep CognitionGenerate stories using RNNs |pure Mathematics with code|:Zero Padding•Zero padding adds zeros around the borders (...)

#deep-learning #machine-learning #technology #python #artificial-intelligence

DeepLearning 101 : #coursera Vs #udemy Vs #udacity
▻https://hackernoon.com/deeplearning-101-coursera-vs-udemy-vs-udacity-b4eb3de06dbe?source=rss---

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The era of self-learningDeep Learning has taken the world by storm and the juggernaut has kept rolling since early 2017.As far as the core methodology goes, neural networks have been around since decades and convolutional neural networks and recurrent neural networks have been around since 15 odd years.What has changed suddenly you ask? GPUs and breakthroughs in automated systems like self driving cars. As Andrew Ng himself says, “I think the other reasons the term deep learning has taken off is just branding. These things are just neural networks with more hidden layers, but the phrase deep learning is just a great brand, it’s just so deep.”Developers all around the globe are heavily motivated seeing the innovation DL is driving in each and every sector. Every company is either pitching (...)

#machine-learning #deep-learning

Someone scraped 40,000 Tinder selfies to make a facial dataset for AI experiments
▻https://techcrunch.com/2017/04/28/someone-scraped-40000-tinder-selfies-to-make-a-facial-dataset-for-ai-exp

Tinder users have many motives for uploading their likeness to the dating app. But contributing a facial biometric to a downloadable data set for training convolutional neural networks probably wasn’t top of their list when they signed up to swipe. A user of Kaggle, a platform for machine learning and data science competitions which was recently acquired by Google, has uploaded a facial data set he says was created by exploiting Tinder’s API to scrape 40,000 profile photos from Bay Area (...)

#Tinder #algorithme #facial

These old black-and-white photos were colorized by artificial intelligence
▻http://www.theverge.com/2016/4/27/11519272/these-old-black-and-white-photos-were-colorized-by-artificial

Researchers at Waseda University in Tokyo have created a way to realistically colorize black-and-white photos without any human intervention for the first time ever. The team’s approach is based on convolutional neural networks — a type of machine learning originally inspired by the visual cortex of a cat.

#Couleur #Image #Intelligence_artificielle #Japon #Photographie #Waseda_University