#100DaysOfMLCode

My journey through 100DaysOfMLCode Challenge

Day 1

• Started working on Entity Extraction.
• I will be using bi-LSTM + CRF with character embeddings for NER and POS.
• Reading: https://guillaumegenthial.github.io/sequence-tagging-with-tensorflow.html
• Repo: https://github.com/partoftheorigin/EntityExtraction

Day 2

• Almost done with Sequence Tagging.
• Repo: https://github.com/partoftheorigin/EntityExtraction/blob/master/LSTM_CRF_Twitter_NER.ipynb

Day 3

• Working on Automated Question Generation.
• Research papers:
  1. https://arxiv.org/pdf/1706.04560v2.pdf
  2. https://arxiv.org/pdf/1308.0850.pdf
  3. https://arxiv.org/pdf/1706.01450.pdf

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

• Working on Neural QA.
• Preprocessed and saved data to be used afterwards for training.
• Creating word embeddings using GloVe.
• Repo: https://github.com/partoftheorigin/NeuralQuestionAnswer

Day 9

• Read this text classification guide by Google Developers: https://developers.google.com/machine-learning/guides/text-classification/
• Helped a friend with her first ML project, classification using Iris Dataset.
• Will continue work on Automated Question Answer Generation using SQuAD Dataset.

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