GitHub.com 使用 Git 作为版本控制系统(version control system)提供在线源码托管的服务,同时是个有社交功能的开发者社区。 国外类似服务: Bitbucket.com
Gitlab.com
国内类似服务:
Coding.net

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简介:

传统机器学习:

  • 感知机
  • 逻辑回归
  • 线性回归

多层感知机:

  • dropout多层感知机
  • 归一化与多层感知机
  • 反向传播与多层感知机

卷积神经网络:

  • 基础
  • 全连接层
  • LeNet
  • AlexNet
  • VGG
  • DenseNet
  • ResNet
  • 归一化层

自编码器

GANs

GNNs

RNNs

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最新论文

Increasingly larger number of software systems today are including data science components for descriptive, predictive, and prescriptive analytics. The collection of data science stages from acquisition, to cleaning/curation, to modeling, and so on are referred to as data science pipelines. To facilitate research and practice on data science pipelines, it is essential to understand their nature. What are the typical stages of a data science pipeline? How are they connected? Do the pipelines differ in the theoretical representations and that in the practice? Today we do not fully understand these architectural characteristics of data science pipelines. In this work, we present a three-pronged comprehensive study to answer this for the state-of-the-art, data science in-the-small, and data science in-the-large. Our study analyzes three datasets: a collection of 71 proposals for data science pipelines and related concepts in theory, a collection of over 105 implementations of curated data science pipelines from Kaggle competitions to understand data science in-the-small, and a collection of 21 mature data science projects from GitHub to understand data science in-the-large. Our study has led to three representations of data science pipelines that capture the essence of our subjects in theory, in-the-small, and in-the-large.

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