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104-recruit-comprehensive-analysis

2nd-ml100days

blp-demand-estimation

Python code for BLP (Berry, Levinsohn and Pakes) method of structural demand estimation using the random-coefficients logit model. Code for estimation of demand and supply-side moment jointly is also provided.

econml

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

fastai

The fastai deep learning library

figures

R code for figures on Statistics with R slides

final-project2019

kaggle-futursales

ntu_ms_thesis

National Taiwan University Master's Thesis Template (Latex + Word)

pytorch-seq2seq

Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

pytorch-tutorial

PyTorch Tutorial for Deep Learning Researchers

pytorch_timeseries_prediction

quantecon.py

A community based Python library for quantitative economics