Learn how to represent memory in code. Then define and train RNNs in Pytorch and apply them to tasks that involve sequential data
The practical notebook can be found at http://14.232.166.121:8880/lab/workspaces/andy > char_rnn > Character_Level_RNN_Exercise.ipynb
Training Optimization So by now we’ve learned how to build a deep neural network and how to train it to fit our data. Sometimes however, we go out there and train on ourselves and find Read more…
The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) spots out some major trends in NLP. This post focus on one exciting and clever paper that motivate the more careful usage of Read more…
Introduction A hyper parameter is a variable that we need to set before applying a learning algorithm into a dataset. The challenge with hyper parameters is that there are no magic numbers that work everywhere. Read more…