Implementation of Everybody Dance Now by pytorch https://t.co/8etHXuQyVZ #deeplearning #machinelearning #ml #ai #neuralnetworks #datascience #pytorch
— PyTorch Best Practices (@PyTorchPractice) September 15, 2018
Implementation of Everybody Dance Now by pytorch https://t.co/8etHXuQyVZ #deeplearning #machinelearning #ml #ai #neuralnetworks #datascience #pytorch
— PyTorch Best Practices (@PyTorchPractice) September 15, 2018
Lazy man's version: https://t.co/eZdohNWP1l
— Andres Torrubia (@antor) September 15, 2018
Neural nets used:
— Janelle Shane (@JanelleCShane) September 15, 2018
Writes text letter-by-letter: https://t.co/p1SEkv9nGb
Writes text word-by-word: https://t.co/3OICLYIcFa
The outputs with nonsense words were mostly from the letter-by-letter neural net.
Not a very elegant solution, but it gets the job done: https://t.co/5ZqYeh50gV pic.twitter.com/rh3o0VDcjd
— Eric (@expersso) September 12, 2018
GitHub repo of jupyter notebooks from a Columbia Journalism course on algorithmic data analysis in journalism & journalism coverage of algorithms: https://t.co/DsvwXS68DA
— Rachel Thomas (@math_rachel) September 10, 2018
💖 fresh modeling material from @topepos & Kjell Johnson:
— Mara Averick (@dataandme) September 9, 2018
📖 "Feature Engineering and Selection: A Practical Approach for Predictive Models" https://t.co/q6TUcTsfkI
* data and #rstats code: https://t.co/zrLDd8hMuf pic.twitter.com/7Ag298iZiW
🗞📦 geo-classifier now on CRAN…
— Mara Averick (@dataandme) September 5, 2018
🗺 "newsmap: geographical news classifier" by Kohei Watanabehttps://t.co/eYumw2ESAJ #rstats #textanalysis #MachineLearning pic.twitter.com/0HLxjKMPII
Deep Exemplar-based Colorization
— ML Review (@ml_review) September 4, 2018
The first end-to-end deep learning approach to controllable colorisation.
ArXivhttps://t.co/XxcQlcIV3c
Githubhttps://t.co/DwEwtp3469 pic.twitter.com/znDMZcnB1L
1st place solution for @Kaggle Home Credit Default Risk Competition: https://t.co/8nstdRWGcX
— Bojan Tunguz (@tunguz) September 2, 2018
Tensorflow implementation of Adversarial Autoencoders https://t.co/IPBmE3u69h #deeplearning #machinelearning #ml #ai #neuralnetworks #datascience #tensorflow
— TensorFlow Best Practices (@TFBestPractices) September 2, 2018
Pytorch implementation of the deep dream computer vision algorithm https://t.co/pgvPiHGGuQ #deeplearning #machinelearning #ml #ai #neuralnetworks #datascience #pytorch
— PyTorch Best Practices (@PyTorchPractice) September 1, 2018
There does not seem to be a lot of information available on training large detection models
— Radek Osmulski (@radekosmulski) September 1, 2018
Here is an overview of how I trained yolov3 with SPP on the Open Images dataset: https://t.co/lMVjzM3VOy
I am also sharing the config files and trained weights: https://t.co/KaiSkl9DtR