One of the most striking facts about neural networks is that they can compute any function at all. That is, suppose someone hands you some complicated, wiggly function, f(x)f(x): No matter what the function, there is guaranteed to be a neural network…

http://neuralnetworksanddeeplearning.com/chap4.html In essence, we're using our single-layer neural networks to build a lookup table for the function. And we'll be able to build on this idea to provide a general proof of universality.…

The human visual system is one of the wonders of the world. Consider the following sequence of handwritten digits: Most people effortlessly recognize those digits as 504192. That ease is deceptive. In each hemisphere of our brain, humans have a prima…

第七部分 让 学习率 和 学习潜能 随时间的变化 光训练就花了一个小时的时间.等结果并非一个令人心情愉快的事情.这一部分.我们将讨论将两个技巧结合让网络训练的更快! 直觉上的解决的方法是,開始训练时取一个较高的学习率,随着迭代次数的增多不停的减小这个值.这是有道理的,由于開始的时候我们距离全局最长处很远.我们想要朝着最长处的方向大步前进:然而里最长处越近,我们就前进的越慎重,以免一步跨过去.举个样例说就是你乘火车回家,但你进家门的时候肯定是走进去.不能让火车开进去. 从讨论深度学习中初始化和学习…

Machine Learning, Homework 9, Neural NetsApril 15, 2019ContentsBoston Housing with a Single Layer and R package nnet 1Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Digit Recognition wit…

Using convolutional neural nets to detect facial keypoints tutorial   this blog from: http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/   December 17, 2014 | categories: Python, Deep Learning…

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…

two important types of artificial neuron :the perceptron and the sigmoid neuron Perceptrons 感知机的输入个数不限,每个输入的取值都是二元的(0或1,这点不确定,后续确认下),输出是0或1. Sigmoid neuron Sigmoid neurons are similar to perceptrons, but modified so that small changes in their weight…

Chapter1 使用神经网络辨识手写数字 人类的视觉系统是自然界的一大奇迹.试看如下的手写数列: 绝大多数人都能毫不费劲地认出这些数字是504192,而这会让人产生识别数字非常简单的错觉.人类大脑的每个半球都有初级视觉皮层,其中一个可以被记作V1,包含有1亿4千万的神经元以及它们之间数以百亿计的相互连接.何况人类的视觉系统不仅只有V1,而还包括其他所有的初级视觉皮层:V2,V3,V4和V5,逐步负责着不同复杂程度的图像处理.我们的大脑里有一台超级计算机,经过数亿年的进化,能够极好地适应理解视觉…

<Neural Network and Deep Learning>_chapter4: A visual proof that neural nets can compute any function文章总结(前三章翻译在百度云里) 链接:http://neuralnetworksanddeeplearning.com/chap4.html: Michael Nielsen的<Neural Network and Deep Learning>教程中的第四章主要是证明神经网络可以用…