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advanced-factorization-of-machine-systems

GSOC 2017 - Apache Organization - # Implementation of Factorization Machines on Spark using parallel stochastic gradient descent (python and scala)

ailearning

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

algonotes

公众号【浅梦的学习笔记】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

algorithm_interview_notes-chinese

2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记

alphafm

Multi-thread implementation of Factorization Machines with FTRL for binary-class classification problem.

angel

A Flexible and Powerful Parameter Server for large-scale machine learning

anyq

FAQ-based Question Answering System

asrt_speechrecognition

A Deep-Learning-Based Chinese Speech Recognition System 基于深度学习的中文语音识别系统

byteps

A high performance and generic framework for distributed DNN training

chatbot

具有情景及舆情的聊天机器人

chatbotcourse

自己动手做聊天机器人教程

chatgpt_principle_fine-tuning_code_paper

本『ChatGPT资源库(原理/微调/代码/论文)』的初始版本来自July CSDN博客上阅读量高达50万的ChatGPT系列，联合发起人：七月ChatGPT原理课学员，6月初正式对外发布

chinese-chatllama

中文LLaMA基础模型；中文ChatLLaMA对话模型；NLP预训练/指令微调数据集

chinese-qa-with-bert

基于预训练模型 BERT 的阅读理解

classification

感知器、贝叶斯分类、决策树分类、K最近邻法、逻辑回归、支持向量机...

cmdline

A Command Line Parser

cnn

cnn-1

Convolutional Neural Network using Eigen and C++

code-server

VS Code in the browser

conversationalrobotdesign

ctr_prediction

CTR prediction using FM FFM and DeepFM

data-science-notes

数据科学的笔记以及资料搜集

data-warehouse

电商平台数据仓库搭建

dcn

build up a deep & cross network in tensorflow from scratch 从头开始搭建一个 deep and cross网络

deep_ctr

deepinterestnetwork

deeplearning

Python for “Deep Learning”，《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，近30万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

deeplearning-mxnet

Mxnet for CTR

deeplearntoolbox

Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.