• cross entropy loss is not quite the same as optimizing classification accuracy. Althougth the two are correlated.
  • It's not necessarily true that Deep learning approaches are often said to require enormous amount of data to work well. In this competitation, there'are 30,000 examples for 121 classes.
    To achieve this, some tricks are :

    • dropout
    • weight decay
    • data argumentation
    • pre-training
    • pseudo-labeling
    • parameter sharing
  • The method is implemented based on Theano:
    • Python, Numpy, Theano, cuDNN, PyCUDA, Lasagne
    • scikit-image: pre-processing and data argumentation
    • ghalton: quasi-random number generation
  • Hardware:
    • GTX 980, GTX 680, Tesla K40
  • Pre-processing and data argumentation:
    • Normalization: pre-pixel zero mean unit variance

to be finished

"Classifying plankton with deep neural networks" notes的更多相关文章

  1. Classifying plankton with deep neural networks

    Classifying plankton with deep neural networks The National Data Science Bowl, a data science compet ...

  2. Training Deep Neural Networks

    http://handong1587.github.io/deep_learning/2015/10/09/training-dnn.html  //转载于 Training Deep Neural ...

  3. 课程一(Neural Networks and Deep Learning)，第四周（Deep Neural Networks） —— 3.Programming Assignments: Deep Neural Network - Application

    Deep Neural Network - Application Congratulations! Welcome to the fourth programming exercise of the ...

  4. Training (deep) Neural Networks Part: 1

    Training (deep) Neural Networks Part: 1 Nowadays training deep learning models have become extremely ...

  5. 为什么深度神经网络难以训练Why are deep neural networks hard to train?

    Imagine you're an engineer who has been asked to design a computer from scratch. One day you're work ...

  6. [C4] Andrew Ng - Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

    About this Course This course will teach you the "magic" of getting deep learning to work ...

  7. On Explainability of Deep Neural Networks

    On Explainability of Deep Neural Networks « Learning F# Functional Data Structures and Algorithms is ...

  8. Introduction to Deep Neural Networks

    Introduction to Deep Neural Networks Neural networks are a set of algorithms, modeled loosely after ...

  9. 深度神经网络入门教程Deep Neural Networks: A Getting Started Tutorial

    Deep Neural Networks are the more computationally powerful cousins to regular neural networks. Learn ...

随机推荐

  1. [转]C# 中的.pdb/ .vshost.exe/ .vshost.exe.manifest文件讨论

    原文出处:http://blog.163.com/chwei_sunshine/blog/static/19412628320125893656652/ pdb文件: 英文全称:Program Dat ...

  2. Crystal Report分組中的序號重新遞增

    客戶要批次列印發票,也就是報表需要按照發票號碼(INV_NO)進行分組,每個發票里還有明細的item,之前因為直接抓RecordNumber,所以該欄位只能從1開始計數,遇到新的發票發號不會重新從1開 ...

  3. ios 添加多个target 管理 多个版本文件

    1. 添加一个Target 这里是添加一个Test 项目 这里添加新的target Test与Release 也是同上的操作

  4. ubuntu安装体验

    本文记录一下昨晚及今天安装ubuntu系统的过程及体验 2016年6月13日09:36:11 更新 今天才有发现原来自己有个没填的坑 = = 那次安乌班图后第一感觉是很好用,新鲜了好几天,但是很快,新 ...

  5. Floyd算法(弗洛伊德算法)

    算法描述: Floyd算法又称为弗洛伊德算法,插点法,是一种用于寻找给定的加权图中顶点间最短路径的算法.从图的带权邻接矩阵A=[a(i,j)] n×n开始,递归地进行n次更新,即由矩阵D(0)=A,按 ...

  6. USB驱动开发

    1.usb特点 2.usb class 3.

  7. phonegap退出android程序

    最近用android做了一个程序,在点“后退”的时候,会不停地后退,感觉不好. 查了些资料有这么些: 一.toast_plugin插件 <script type="text/javas ...

  8. VB.NET生成Excel，已存在提示框点否时报错

    如题 Exception from HRESULT: 0x800A03EC 最终没有好的解决方案,只好屏蔽掉 Try obook.SaveAs(excelSaveName) Catch ex As S ...

  9. Data Mining 概念

    数据挖掘概念: 数据挖掘是在大型数据库中.自动的发现有用信息的过程. 然. 这个有用只是一个感性的东西.比如我们从表中索引一行数据.这个算不上数据挖掘.因为它依赖的是数据的明显特征. 数据挖掘基本步骤 ...

  10. USB HID复合设备实例—键盘+鼠标

    实现这种USB HID复合设备有两种方法,在<USB HID协议入门>一节已经讲到其中一种方法,说一个USB HID设备可以包含多种功能的报告描述符合集,这样可以实现复合设备,如带鼠标功能 ...