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I always enjoy making my contribution to this course at UCL; gives me an opportunity to rediscover my field and to experiment with new ways of explaining. Here's my lecture: https://t.co/cxWaXH8QSM
Google Brain team researchers @ekindogus, @barret_zoph, and @quocleix have open sourced the AutoAugment image classification policies that achieve state-of-the-art results. Check out the the paper and code is located in tensorflow/models/research/autoaugment. #AutoML
Just finished writing final few chapters of Machine Learning Yearning book draft, on how to organize and strategize your ML projects. Will send out soon -- sign up at https://t.co/TgvKPa033r if you want a copy!
Announcing Open Images V4, the largest existing dataset with object location annotations, along with the Open Images Challenge, a new object detection challenge to be held at the 2018 European Conference on Computer Vision (#ECCV2018). Learn more at https://t.co/3JeXmQ0CYQ
Check out AmoebaNets, state-of-the-art programmatically evolved #NeuralNetworks from the Google Brain team (https://t.co/6BSGMMfnll), and one of the latest results from our broader #AutoML efforts → https://t.co/GNKMeCmhtN
Check out deep learning for analyzing microscopy images, especially handling out-of-focus images, using a pre-trained @TensorFlow model with plug-ins from the Google Accelerated Science team. https://t.co/cu93rOohqv
In order to make Generative Adversarial Networks (GANs) easier to experiment with, we’ve open sourced TFGAN, a lightweight #TensorFlow library designed to make it easy to train and evaluate GANs ➝ https://t.co/EkNnDj3VxC