jatinkhilnani Goto Github PK

20190507nyu

2019/05/07 Lab session for Deep learning

ancient-text-restoration

Restoring ancient text using deep learning: a case study on Greek epigraphy.

consumer_complaints

datascience-exam

Repo supporting data science team interview exams.

datasciencecourse

This holds iPython notebooks and lecture slides for the Intro to Data Science Master's course I teach at NYU.

goldeneye

Quickly look up 4-letter bird alpha codes

gramps

Source code for Gramps Genealogical program

mesa

Mesa is an agent-based modeling framework in Python

muda

A library for augmenting annotated audio data

pytorch-deep-learning

Minicourse in Deep Learning with PyTorch

redditscoreforecast_fall2018

Content aggregators collect content from across the web in order to attract internet traffic, creating revenue through advertisements. These aggregator websites compete to identify viral content early to maximize their share of the internet traffic. This project focused on analyzing real-time post data from Reddit, a collection of re-posts and original content, to identify posts that would become viral. The problem was framed as a binary classification problem where a viral Reddit post had a score equal or greater than 3656. In order to predict posts which posts will become viral, logistic regression and random forest models were run on Reddit submission data obtained from several days of continuously scraping data. In addition to hyperparameter tuning and feature engineering, one hot encoding using character-embedding was stacked onto the classification models to further improve model performance. The models provide reliable predictions of potentially viral posts, laying the foundation for building a robust and computationally-efficient pipeline in a real business setting.

speaker-diarization

speaker diarization by uis-rnn and speaker embedding by vgg-speaker-recognition

speedchallenge

The comma.ai Speed Prediction Challenge!

text-as-data-lab-spring-2019

Materials for the lab component of DS-GA 1015 Text-as-Data (Spring 2019).

transformer