转自知乎 这货就是基于 SE-Net [5]中的 Squeeze-and-Excitation module 来进行进一步拓展 具体来说,文中把 channel-wise attention 看成是教网络 Look 'what’:而spatial attention 看成是教网络 Look 'where',所以它比 SE Module 的主要优势就多了后者 ------------------------------------ 我们先看看 SE-module: SE-module 流程: 将输

向量的一种特殊乘法 element wise multiplication 物体反射颜色的计算采用这样的模型: vec3 reflectionColor = objColor * lightColor;//物体反射颜色 = 物体颜色 * 光源颜色. (vec3(r,g,b), r,g,b在[0,1]范围里). 比如:光源是自然光:lightColor = vec3(1.0,1.0,1.0);物体是绿色的:objColor  = vec3(0,0,1.0,0.0); 反射光就是:reflectio

An Intuitive Explanation of Convolutional Neural Networks https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ Posted on August 11, 2016 by ujjwalkarn What are Convolutional Neural Networks and why are they important? Convolutional Neural

https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ An Intuitive Explanation of Convolutional Neural Networks Posted on August 11, 2016 by ujjwalkarn What are Convolutional Neural Networks and why are they important? Convolutional Neural

Sequence Models This is the fifth and final course of the deep learning specialization at Coursera which is moderated by deeplearning.ai Here are the course summary as its given on the course link: This course will teach you how to build models for n

About this Course This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applica

About this Course This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good res

About this Course If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "s

本文申明:本系列的所有实验数据都是来自[美]Peter Harrington 写的<Machine Learning in Action>这本书,侵删. 一案例导入:玛利亚小姐最近寂寞了,然后她就准备在一个在线社交网站搞网恋,但是凡是都有一个选择,按照她以往的经验,她接触了三种人: 1:不喜欢的人 2:魅力一般的人 3:特别有魅力的人 但是啊,尽管发现了这三类人,但是她还是无法甄别她究竟喜欢哪种人.所以她就求助我们,如果给她当这个月老.---------那我们就把这个实践叫做月老实践吧. 二案

Adit Deshpande CS Undergrad at UCLA ('19) Blog About A Beginner's Guide To Understanding Convolutional Neural Networks Part 2 Introduction Link to Part 1 In this post, we’ll go into a lot more of the specifics of ConvNets. Disclaimer: Now, I do reali

Adit Deshpande CS Undergrad at UCLA ('19) Blog About A Beginner's Guide To Understanding Convolutional Neural Networks Introduction Convolutional neural networks. Sounds like a weird combination of biology and math with a little CS sprinkled in, but

---恢复内容开始--- < Machine Learning 机器学习实战>的确是一本学习python,掌握数据相关技能的,不可多得的好书!! 最近邻算法源码如下,给有需要的入门者学习,大神请绕道. 数字识别文件 ''' Created on Sep 16, 2010 kNN: k Nearest Neighbors Input: inX: vector to compare to existing dataset (1xN) dataSet: size m data set of know

Understanding Convolution in Deep Learning Convolution is probably the most important concept in deep learning right now. It was convolution and convolutional nets that catapulted deep learning to the forefront of almost any machine learning task the

内容主要来源于机器学习实战这本书.加上自己的理解. 1.KNN算法的简单描写叙述 K近期邻(k-Nearest Neighbor.KNN)分类算法能够说是最简单的机器学习算法了. 它採用測量不同特征值之间的距离方法进行分类. 它的思想非常easy:假设一个样本在特征空间中的k个最相似(即特征空间中最邻近)的样本中的大多数属于某一个类别.则该样本也属于这个类别. 下图是大家引用的一个最经典演示样例图. 比方上面这个图,我们有两类数据,各自是蓝色方块和红色三角形,他们分布在一个上图的二维中间中. 那

,400],[200,5],[100,77],[40,300]]) shape:显示(行,列)例:shape(group)=(4,2) zeros:列出一个同样格式的空矩阵,例:zeros(group)=([[0,0],[0,0],[0,0],[0,0]]) tile函数位于python模块 numpy.lib.shape_base中,他的功能是反复某个数组.比方tile(A,n),功能是将数组A反复n次,构成一个新的数组 sum(axis=1)矩阵每一行向量相加 3.数据集   4.代码  

python机器学习实战(一) 版权声明:本文为博主原创文章,转载请指明转载地址 www.cnblogs.com/fydeblog/p/7140974.html  前言 这篇notebook是关于机器学习中监督学习的k近邻算法,将介绍2个实例,分别是使用k-近邻算法改进约会网站的效果和手写识别系统.操作系统:ubuntu14.04    运行环境:anaconda-python2.7-notebook    参考书籍:机器学习实战      notebook  writer ----方阳  k-

0. 小试牛刀 首先,激活tensorflow环境( source activate tensorflow ),随后在ipython里: import tensorflow as tf sess = tf.Session() 创建常量格式如下: tf.constant(value, dtype=None, shape=None, name='Const', verify_shape=False) 例1: node1 = tf.constant(3.0, dtype=tf.float32) nod

刚开始学习机器学习,先跟这<机器学习实战>学一些基本的算法 ----------------------------------分割线-------------------------------------------- 该算法是用来判定一个点的分类,首先先找到离该点最近的k个点,然后找出这k个点的哪种分类出现次数最多,就把该点设为那个分类距离公式选用欧式距离公式: 下面给出例子(来自<机器学习实战>) 1.约会对象喜欢程度的判定: 现需要一个约会对象喜欢程度的分类器 给定数据集

关于本文说明,本人原博客地址位于http://blog.csdn.net/qq_37608890,本文来自笔者于2017年12月04日 22:54:26所撰写内容(http://blog.csdn.net/qq_37608890/article/details/78714664).     本文根据最近学习机器学习书籍 网络文章的情况,特将一些学习思路做了归纳整理,详情如下.如有不当之处,请各位大拿多多指点,在此谢过. 一.k-近邻算法(k-Nearest Neighbor,KNN)概述 1.简

使用二维数据构造简单卷积神经网络 觉得有用的话,欢迎一起讨论相互学习~Follow Me 图像和一些时序数据集都可以用二维数据的形式表现,我们此次使用随机分布的二位数据构造一个简单的CNN-网络卷积-最大池化-全连接 参考代码 # Implementing Different Layers # --------------------------------------- # # We will illustrate how to use different types # of layers

使用一维数据构造简单卷积神经网络 觉得有用的话,欢迎一起讨论相互学习~Follow Me 神经网络对于一维数据非常重要,时序数据集.信号处理数据集和一些文本嵌入数据集都是一维数据,会频繁的使用到神经网络.我们在此利用一组一维数据构造卷积层-最大池化层-全连接层的卷积神经网络.希望给大家使用CNN处理一维数据一些帮助. 参考代码 # Implementing Different Layers # --------------------------------------- # # We will