shenweichen

浅梦's starred repositories

How-To-Ask-Questions-The-Smart-Way

本文原文由知名 Hacker Eric S. Raymond 所撰寫，教你如何正確的提出技術問題並獲得你滿意的答案。

Language:JavaScriptMIT30625 417 29

catboost

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Language:PythonApache-2.08028 191 2320

DeepCTR

Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

Language:PythonApache-2.07539 178 369

deap

Distributed Evolutionary Algorithms in Python

Language:PythonLGPL-3.05794 191 517

x-deeplearning

An industrial deep learning framework for high-dimension sparse data

Language:PureBasicApache-2.04252 231 346

GraphEmbedding

Implementation and experiments of graph embedding algorithms.

Language:PythonMIT3682 64 64

Tensorflow-Project-Template

A best practice for tensorflow project template architecture.

Language:PythonApache-2.03604 152 20

rtb-papers

A collection of research and survey papers of real-time bidding (RTB) based display advertising techniques.

3516 479 8

RL-Adventure

Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL

Language:Jupyter Notebook3023 72 22

DeepCTR-Torch

【PyTorch】Easy-to-use,Modular and Extendible package of deep-learning based CTR models.

Language:PythonApache-2.02984 46 135

awesome-jupyterlab

A curated list of awesome JupyterLab extensions and resources

2517 84 11

DeepMatch

A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors which can be used for ANN search.

Language:PythonApache-2.02216 51 74

AlgoNotes

【浅梦学习笔记】文章汇总:包含排序&CXR预估，召回匹配，用户画像&特征工程，推荐搜索综合计算广告，大数据，图算法，NLP&CV，求职面试等内容

1577 310

bottom-up-attention

Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome

Language:Jupyter NotebookMIT1426 26 117

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.

Language:Python1422 52 1