2018 DS/ML digest 20

2018 DS/ML digest 20

Posted by snakers41 on August 12, 2018

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2018 DS/ML digest 20

Market / posts:
(1) Classic AI competition https://habr.com/post/419745/

  • Looks really cool, but there is no leaderboard
  • Also too little time

(2) FAST AI and their fast imagenet training http://www.fast.ai/2018/08/10/fastai-diu-imagenet/

  • Too much hype and over engineering?
  • Sound ideas: (1) using variable image size (because CNNs are FCNs inside) (2) progressive resizing (train on smaller images => then increase the size, also you can use larger batch-sizes) (3) remove weight decay from batchnorm layers
  • PyTorch has a distributed training tutorial https://pytorch.org/tutorials/intermediate/dist_tuto.html#distributed-training

(3) Intel NLP overview https://software.intel.com/en-us/articles/transfer-learning-in-natural-language-processing

  • Drop connect for LSTMs (randomly set innet LSTM weights to zero)
  • Concat pooling of LSTM mean output and last output
  • Gradual unfreezing, different learning rates per layer

(4) Kaggle provides free GPUs … yeah right https://mailchi.mp/kaggle/tap-into-the-power-of-gpus-for-deep-learning-2577173?e=fbd638a11f

(5) Recurrent models do not need to be recurrent http://www.offconvex.org/2018/07/27/approximating-recurrent/

(6) The state of conversational AI -

Papers / intersting abstracts:

Applied code / libraries