AI is not inscrutable magic -- it's math and data and computer programming, made by regular humans. People who make AI are not unicorns. They are just people who like math and data and computer programming.
Great resource! A Survival Guide to Data Science with R – using templates (w/ R code) #datascience #MachineLearning #DataAnalytics #Statistics #rstats togaware.com/onepager/
Easy and simple goodness! I love how ml is coming along!
GluonCV — Deep Learning Toolkit for Computer Vision bit.ly/2sSM53m #AI #DeepLearning #MachineLearning #DataScience
Simple Tensorflow implementation of "Self-Attention GAN" (Han Zhang, Ian Goodfellow) reddit.com/r/MachineLearn…
More resources: Announcing Open Images V4 and the ECCV 2018 Open Images Challenge buff.ly/2rEzrV7 #AI #DeepLearning #MachineLearning #DataScience
If you are still at #NAACL2018 come to talk with @OmniaHZayed abt our work on phrase-level metaphor identification at the #FigLang18 poster session this afternoon ;) #NLProc @insight_centre @DSIatNUIG
Really useful! Multiple output classification with Keras (step by step guide w/ Py code) #DataScience #MachineLearning #DeepLearning pyimagesearch.com/2018/06/04/ker…
.@MayoClinic researchers use #deeplearning to classify ultrasound images for an affordable early detection solution in the fight against breast cancer. nvda.ws/2JuSX14
Let's learn the complete process of Web Scraping in R and gain expertise to use any type of data available over the Internet, with this guide, here. buff.ly/2vJtag2 #MachineLearning #Rstats
😻 the handiest: "Probability Cheat Sheets" by @wzchen & @stat110 buff.ly/2wxia4o #probability #statistics #SoDS18
hadrienj.github.io/posts/Deep-Lea… For anyone wanting to learn enough linear algebra to have a good understanding of it in a few days (assuming you have a mathematics background in calculus and statistics already) this series based on Ian Goodfellow et al. Deep learning book is exceptional!
What is AI, really? – AI-First Design – Medium bit.ly/2rqPpBN #AI #DeepLearning #MachineLearning #DataScience
Our work w/ @mblondel_ml 'Differentiable Dynamic Programming for Structured Prediction and Attention' was accepted at @icmlconf ! arxiv.org/abs/1802.03676 Sparsity and backprop in CRF-like inference layers using max-smoothing, application in text + time series (NER, NMT, DTW)
Tutorial to help you understand the workings of a #recommendationsystem with a real life example of a bank. Try out this technique, here. buff.ly/2wtnPK4 #Analytics
#Robots learning how to run outside with #autonomous navigation >> @BostonDynamics via @MikeQuindazzi >> #ai #deeplearning #machinelearning #bigdata #iot #robotics #autonomousvehicle #atlas
Backprop / hopfield networks / Self organizing maps /lateral connections/ Bolzmann machines @kchonyc a retrospective
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