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awesome-deep-learning-papers-for-search-recommendation-advertising

Awesome Deep Learning papers for industrial Search, Recommendation and Advertising. They focus on Embedding, Matching, Ranking (CTR and CVR prediction), Post Ranking, Multi-task Learning, Graph Neural Networks, Transfer Learning, Reinforcement Learning, Self-supervised Learning and so on.

bandit_algo_evaluation

Offline evaluation of multi-armed bandit algorithms

bandits

Multi-Armed Bandit algorithms applied to the MovieLens 20M dataset

bottle

bottle.py is a fast and simple micro-framework for python web-applications.

connected-component

Map Reduce Implementation of Connected Component on Apache Spark

coursera

Script for downloading Coursera.org videos and naming them.

courses

fast.ai Courses

deepinterestnetwork

dien

dl-pytorch

dlrm

An implementation of a deep learning recommendation model (DLRM)

dotfiles

dpark

Python clone of Spark, a MapReduce alike framework in Python

educative-io-contents

emacs.d

free-programming-books

:books: Freely available programming books

glog

C++ implementation of the Google logging module

grokking-the-coding-interview-patterns-for-coding-questions

grokking-the-system-design

Grokking the system design interview course materials

guava

Google core libraries for Java

gym

A toolkit for developing and comparing reinforcement learning algorithms.

howdoi

howdoi - instant coding answers via the command line

ibook

收藏一些电子书

jinja2

The Jinja2 template engine

learn-python

小白的Python入门教程：部分章节源码

lisp-koans

Common Lisp Koans is a language learning exercise in the same vein as the ruby koans, python koans and others. It is a port of the prior koans with some modifications to highlight lisp-specific features. Structured as ordered groups of broken unit tests, the project guides the learner progressively through many Common Lisp language features.

mimn

personalized-news-recommendation

Multi Armed Bandits implementation using the Yahoo! Front Page Today Module User Click Log Dataset

portia

Visual scraping for Scrapy

practical_common_lisp