Hetian Bai's repositories
DS-GA-1004-Big-Data-Correlation-Discovery-Cross-Multiple-Datasets
Big Data Term Project: Manipulate on very large datasets with Hadoop map-reduce, Spark
translate_machine_translation
Vietnamese and Chinese to English
1001_Machine_Learning_Project
A data science course project that uses several machine learning algorithms to analyze data
cs231n.github.io
Public facing notes page
Face-recognition
This is the course project for DS-GA 1008 Deep Leaning by Prof. Yann LeCun
Plated_Recipe_Tags_Predict
Capstone project--collaborated with Plated.com
Wikileaks-Stock-Market-1
Machine Learning course term project, collaborated with Dr. Daniel Chen, and Dr. Elliot Ash
DeepNLP-models-Pytorch
Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)
fastText
Library for fast text representation and classification.
JDD-Login-Risk-Detection
京东金融赛登录风险识别(算法组)8th
Machine-Learning-2018-Project-
A repository for Machine Learning Term project
mlcourse
Machine learning course materials.
MMT
Neural Adaptive Machine Translation that adapts to context and learns from corrections.
mxnet-the-straight-dope
An interactive book on deep learning. Much easy, so MXNet. Wow.
NLP-Movie-Sentiment-Analysis
This is the assignment 1 of DS-GA 1011 Natural Language Processing and Representaton
pybind11
Seamless operability between C++11 and Python
RAKE-Keyword-Extraction
Keyword extraction using standard RAKE algorithm after pre-processing the text with lemmatization, filtering unwanted parts-of-speech and other techniques.
RNN-CNN-based-Natural-Language-Inference
RNN and CNN models to tackle Stanford Natural Language Inference (SNLI) task
rtb-papers
A collection of research and survey papers of real-time bidding (RTB) based display advertising techniques.
Surrogates-Replication-Code
Code to replicate GAIN application in Athey, Chetty, Imbens and Kang (2019) using simulated employment data
text_classification
all kinds of text classificaiton models and more with deep learning
vistvfinales
Script that compares the average performance of a television show to its finale, identifying shows that surprise and disappoint