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title: Deep Learning Assignment 2

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# Deep Learning Assignment 2

### a: NLP (NOT DONE)
Please discuss the recent trend of rapidly increasing sizes of NLP architectures. 

Natural Language Processing (NLP) back in the day had many problems because it is very context dependent. For example if 10 layers are trying to understand a sentence, there is a lot of grammar and order of words and such involved, and it is complicated to drag all this information through the layers.
But then "few shot learning" or "meta learning" was invented, which made it possible to make huge networks, as the network can now get context of similar sentences which it can use to understand.






## Problem 2:
Below is attached a script for generating a data set to learn translation of dates between multiple human readable and a machine readable format (ISO 8601). 

Task: Using an encoder-decoder setup, perform translation from human readable to machine readable formats. Please express the performance of your trained network in terms of average accuracy of the translated output (so, accuracy on a per-character basis). 

 The task can be solved in numerous different ways. Here are some examples of solutions of similar problems, for inspiration: 

https://jscriptcoder.github.io/date-translator/Machine%20Translation%20with%20Attention%20model.html

https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html

Script for generating data set:

date_translation_dataset






