这篇文章很有价值,但翻译了一段,实在翻译不下去了,没办法,只能转载了. 英文地址:http://blogs.msdn.com/b/adonet/archive/2014/10/21/ef7-what-does-code-first-only-really-mean.aspx A while back we blogged about our plans to make EF7 a lightweight and extensible version of EF that enables new
原文:http://developer.yahoo.com/performance/rules.html 提升网站加载速度的一些优化技巧,大部分在前端层面. 不知道是多久以前写的,看起来有些已经过时了? ==== The Exceptional Performance team has identified a number of best practices for making web pages fast. The list includes 35 best practices divid
Python Standard Library "We'd like to pretend that 'Fredrik' is a role, but even hundreds of volunteers couldn't possibly keep up. No, 'Fredrik' is the result of crossing an http server with a spam filter with an emacs whatsit and some other stuff be
前言: CNN作为DL中最成功的模型之一,有必要对其更进一步研究它.虽然在前面的博文Stacked CNN简单介绍中有大概介绍过CNN的使用,不过那是有个前提的:CNN中的参数必须已提前学习好.而本文的主要目的是介绍CNN参数在使用bp算法时该怎么训练,毕竟CNN中有卷积层和下采样层,虽然和MLP的bp算法本质上相同,但形式上还是有些区别的,很显然在完成CNN反向传播前了解bp算法是必须的.本文的实验部分是参考斯坦福UFLDL新教程UFLDL:Exercise: Convolutional Ne
The Exceptional Performance team has identified a number of best practices for making web pages fast. The list includes 35 best practices divided into 7 categories. Looking to optimize your mobile app experience? Check out Flurry Analytics. Filter by
前言: CNN作为DL中最成功的模型之一,有必要对其更进一步研究它.虽然在前面的博文Stacked CNN简单介绍中有大概介绍过CNN的使用,不过那是有个前提的:CNN中的参数必须已提前学习好.而本文的主要目的是介绍CNN参数在使用bp算法时该怎么训练,毕竟CNN中有卷积层和下采样层,虽然和MLP的bp算法本质上相同,但形式上还是有些区别的,很显然在完成CNN反向传播前了解bp算法是必须的.本文的实验部分是参考斯坦福UFLDL新教程UFLDL:Exercise: Convolutional Ne
要求:实现任意层数的NN. 每一层结构包含: 1.前向传播和反向传播函数:2.每一层计算的相关数值 cell 1 依旧是显示的初始设置 # As usual, a bit of setup import time import numpy as np import matplotlib.pyplot as plt from cs231n.classifiers.fc_net import * from cs231n.data_utils import get_CIFAR10_data from