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3d-point-cloud-generation

Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction (AAAI 2018)

ai-recommendersystem

该仓库尝试整理推荐系统领域的一些经典算法模型

ai_tutorial

精选机器学习，NLP，图像识别， 深度学习等人工智能领域学习资料，搜索，推荐，广告系统架构及算法技术资料整理。算法大牛笔记汇总

algorithm-practice-in-industry

搜索、推荐、广告、用增等工业界实践文章收集（来源：知乎、Datafuntalk、技术公众号）

arima

Simple python example on how to use ARIMA models to analyze and predict time series.

arima-and-seasonal-arima

arima-lstm-hybrid-corrcoef-predict

Applied an ARIMA-LSTM hybrid model to predict future price correlation coefficients of two assets

atom

:atom: The hackable text editor

autoint

Implementation of AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks

awesome-causality-algorithms

An index of algorithms for learning causality with data

better-java

Resources for writing modern Java

binaryconnect

Training Deep Neural Networks with binary weights during propagations

bootstrap

The most popular HTML, CSS, and JavaScript framework for developing responsive, mobile first projects on the web.

bootstrapped

Generate bootstrapped confidence intervals for A/B testing in Python.

causaldiscoverytoolbox

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

clashx

classyvision

An end-to-end PyTorch framework for image and video classification

clp

COIN-OR Linear Programming Solver

cnn

A simple and extensible project based on TensorFlow-Slim image classification model library

coco-caption

d3

Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:

darts-

Code for "DARTS-: Robustly Stepping out of Performance Collapse Without Indicators"

db2jupyter

Db2 JSON Examples using Jupyter Notebooks

deepdream

deeplearning

深度学习相关的模型训练、评估和预测相关代码

django

The Web framework for perfectionists with deadlines.

dnc

A TensorFlow implementation of the Differentiable Neural Computer.

dqn

dragonnet

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.