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clickhouse

ClickHouse is a free analytics DBMS for big data

clrs

📚 Solutions to Introduction to Algorithms Third Edition

cn-deep-learning

https://cn.udacity.com/course/deep-learning-nanodegree-foundation--nd101/

code_for_algo_book

《算法的乐趣》随书发布的代码

coding-interview-university

A complete computer science study plan to become a software engineer.

cola

Clean Object-oriented & Layered Architecture

cplusplus-_implementation_of_introduction_to_algorithms

《算法导论》第三版中算法的C++实现

creating_customer_segments

cs224n-learning-camp

darknet

Convolutional Neural Networks

data-science--cheat-sheet

Cheat Sheets

datascicomp

A collection of popular Data Science Competitions

deep-learning

Repo for the Deep Learning Nanodegree Foundations program.

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deep_learning_with_python

Deep_Learning_with Python Book Code Demo

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

deepspeaker-pytorch

Speaker embedding(verification and recognition) using Pytorch

distributed-redis-tool

🔒This is a simple distributed tools based on Redis.

distributed-systems-technologies-and-cases-analysis

《分布式系统常用技术及案例分析》示例源码

dlcv_for_beginners

《深度学习与计算机视觉》配套代码

docker-elk

The ELK stack powered by Docker and Compose.

dog_breed

dog_vs_cat

Udacity猫狗大战毕设

druid

阿里巴巴数据库事业部出品，为监控而生的数据库连接池。阿里云Data Lake Analytics(https://www.aliyun.com/product/datalakeanalytics )、DRDS、TDDL 连接池powered by Druid

dubbo

Apache Dubbo is a high-performance, java based, open source RPC framework.

emacs.d

An Emacs configuration bundle with batteries included

examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

fastai

The fast.ai deep learning library, lessons, and tutorials

finding_donorsdemo

项目概述 在该项目中，你将把你学到的应用监督学习技术和分析数据的能力应用到美国人口普查收集到的数据中，来帮助一个慈善机构确定哪些人最有可能做捐助。首先你要了解普查数据是什么样的。其次，你要使用一系列数据转换和数据前处理技巧来把数据转变成可以工作的形式。你将把你选择的监督学习的算法应用在数据集上，找出最适合的算法。然后，你要优化你选出的这个模型，把他展示给慈善机构。最后，你需要深入探索这个模型以及它的预测，来看一下他对于给定的数据集表现如何。

firstmapreduce