alok-pandey-09 Goto Github PK

alok-pandey-09

Config files for my GitHub profile.

app-building-crashcourse

AI App Building Crash Course

cs207-2016

CS207 Fall 2016 Public Repository

dashboard_data

explorations

hotel_recommendation_system

hurricane_damaged_building

The latest hurricane - Hurricane Iota, had 61 total fatalities, and 41 are still missing. After a hurricane, damage assessment is vital to the relief helpers and first responders so that resources and help can be planned and allocated appropriately. One way to measure the damage is to detect and quantify the number of damaged buildings, usually done by driving around the affected area and noting down manually. This process can be labor-intensive and time-consuming and not the most efficient method as well. I am trying to build a model that helps in predicting buildings that were damaged by hurricane to help relief teams identify which areas need most help.

named_entity_recognition

Named Entity Recognition (NER) is one of the most popular applications of Natural Language Processing. Initially, we will focus on creating a NER model that identifies key tokens and classifies them into set of predefined entities. The number of scientific papers published per year has exploded in recent years, strengthening its value as one of the main drivers for scientific progress. In astronomy alone, more than 41,000 new articles are published every year and the vast majority are available either via an open-access model or via pre-print services. Indexing the article’s full-text in search engines helps discover and retrieve vital scientific information to continue building on the shoulders of giants, informing policy, and making evidence-based decisions. Nevertheless, it is difficult to navigate in this ocean of data; finding articles rely heavily on string matching searches and following citations/references. NER helps us extract key information from scientific papers which can help search engines to better select and filter articles. For this, we will first train a language model on WIESP dataset. We will define and train a simple RNN network with 2 RNN layers as our baseline model. Then we will play around with BiLSTMs, Time Distributed Layers, BERT and much more.

tensorboard_tutorials

Tensorflow Tutorials

toplay