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#100DaysOfMLCode

My journey through 100DaysOfMLCode Challenge

Day 1

Day 2

Day 3

Day 4

  • Working on Automated Question Generation.
  • Attended a MeetUp on Getting to the Core of Deep Learning.
  • Still understanding the underlying concepts and approaches of the research papers so that we can create our own model from scratch.

Day 5

  • Working on Automated Question Generation.
  • Experimenting with SQuAD2.0 The Stanford Question Answering Dataset

Day 6

  • Working on Automated Question Generation.
  • Started implementation of Context based Question-Answering model based on NeuralQA paper for Stanford SQuAd Dataset.
  • Preparing data now.

Day 7

  • Continuing work on Neural Question Answer Generation.
  • Processing SQuAd Dataset to get it ready for training.
  • Once the data is ready I will create word embeddings using GloVe.

Day 8

Day 9

Day 10

  • Browsed various GitHub repositories
  • Worked on Google's Text Classification guide and Neural QA.

Day 11

  • Completed work on Automated Question Generation.
  • Read research on Fast and Easy Short Answer Grading with High Accuracy - http://www.aclweb.org/anthology/N16-1123
  • Getting hands-on with statistical packages in Python.

Day 12

  • Read research on Neural Arithmetic Logic Units - https://arxiv.org/pdf/1808.00508.pdf
  • Neural networks enhanced with Neural Arithmetic Logic Units (NALU) can learn to track time, perform arithmetic over images of numbers, translate numerical language into real-valued scalars, execute computer code, and count objects in images.
  • Will be coding this paper in PyTorch

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