100daysofcodes

Tracking Papers, Summarize and Codes

Day1:

• Linearn Regression PyTorch Code
• Logistic Regression PyTorch Code
• Nueral Network PyTorch Code
• Basic CNN PyTorch Code

Day2:

• CNN for sentence Classification PyTorch | Link 2
• NN for sentence Classification Fast Text Sklearn Fast Text
• Understanding Convolution in Text L

Day 3:

• PyTorch Helping BooK Link
• Design Challenges and Misconceptions in Neural Sequence Labeling Paper
• POS tagger PyTorch 97.5% acc Link
• Hierarchical Softmax (Helpful for large datasets)
• Basic RNN
• A STRUCTURED SELF-ATTENTIVE SENTENCE EMBEDDING Github | Paper

Day 6:

• CNN+LSTM L
• Word2Vec Summary
• Glove Summary
• FastText Summary

Day 5:

• Review of probability theory L
• PyTorch with examples L
• Best Practice for ML engineer L
• Computational Linear Algebra Fast AI Course
• Deep Learning A Critical Appriasal L

Day 6:

• Universal Language Model Fine-tuning for Text Classification Link
• Improving Language Understanding by Generative Pre-Training
• Deep Pyramid Convolutional Neural Networks for Text Categorization

SoundingBoard: AUser-CentricandContent-DrivenSocialChatbot

• Artificial Paranoia
• The mini-IPIP scales: tiny-yet-effective measures of the Big Five factors of personality.
• Aknowledge-groundedneural conversation model
• Building an architecture for extensibleself-servicespokenlanguageunderstanding
• An introduction to the five-factor model and its applications
• ConversationalAI:Thesciencebehind the Alexa Prize
• Modeling situations in neural chat bots
• A hierarchical latent variable encoder-decoder model for generating dialogues
• Generatinghigh-qualityandinformativeconversationresponseswithsequence-to-sequencemodels
• How to make contextmoreuseful? Anempiricalstudyoncontextaware neural conversational models
• The Anatomy of A.L.I.C.E., chapter Parsing the Turing Test
• A computer program for the study of natural language communication between man and machine