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ad-papers

Papers on Computational Advertising

adversarial_autoencoder

Implementation of Adversarial Autoencoder with Theano

ai-platform

An open-source platform for automating tasks using machine learning models

algorithm-problem-solving

[손고리즘 시즌3] 알고리즘 문제 해결 스터디

autoencoders

An autoencoder tutorial using autograd.

autoencoders_cf

autograd

Efficiently computes derivatives of numpy code.

auxiliary-deep-generative-models

Deep generative models for semi-supervised learning.

awesome-deep-learning

A curated list of awesome Deep Learning tutorials, projects and communities.

awesome-deep-learning-papers

awesome-public-datasets

An awesome list of high-quality open datasets in public domains (on-going).

awesome-tensorflow

TensorFlow - A curated list of dedicated resources http://tensorflow.org

basic-algorithms-season2

[손고리즘-2] (다시 한번) 파이썬을 이용한 알고리즘 기초

bbsvi

Fun with Black-Box Stochastic Variational Inference.

bbvi

Black Box Variational Inference

better-explanations

Warehouse for running small experiments and sharing them with the group

bptf

Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization

causal-inference-tutorial

Repository with code and slides for a tutorial on causal inference.

ccpf

Coupled Compound Poisson Factorization

cdae

Collaborative Denoising Auto-Encoder for Top-N Recommender Systems

cf-nade

A implementation of CF-NADE. Yin Zheng, et. al. "A Neural Autoregressive Approach to Collaborative Filtering", accepted by ICML 2016.

clgp

Categorical Latent Gaussian Process

collab

collabtm

collaborative topic modeling

competitive-data-science-1

How to Win a Data Science Competition: Learn from Top Kagglers

concept-demos

Implementations of ML-related concepts.

convmf-0.1.1

Code for our paper Convolutaional Matrix Factorization for Document Context-Aware Recommendation (RecSys 2016)

cookbook-code

Recipes of the IPython Cookbook, the definitive guide to high-performance scientific computing and data science in Python

ctr

Collaborative modeling for recommendation. Implements variational inference for a collaborative topic models. These models recommend items to users based on item content and other users' ratings.

deep-learning-notes

Experiments with Deep Learning