CaiziLee-CSDN博客

原创 MnistData的读取

这个博客是留给自己备用的#include <iostream>#include <string>#include <vector>#include <fstream>#include <eigen3/Eigen/Core>class MnistData{public: MnistData(std::string train_img_filename,

2017-09-24 14:47:15 638

原创 BP神经网络的实现详解

本文主要详解BP神经网络编程实现，旨在一步一步解析BP神经网络细节，希望能形象明了的阐述BP神经网络，实现原理源自于斯坦福UFLDL教程，原理公式推导不再赘述，但会有些说明，本文程序由C++11实现，矩阵计算基于Eigen3(不熟悉的可以去网上搜索Eigen的使用方法，本文不做叙述)，那么我们开始吧！为了给算法列一个提纲，首先截一个UFLDL教程上关于BP算法的步骤，做个引导：

2017-09-23 20:45:19 1933 1

原创图像升采样的实现详解

此篇文章主要详细描述升采样的实现，基于OpenCV3.2.0&C++实现，升采样实现效果为将M*N的图像采样得到2M * 2N，算法的主要思想如下（图片截自UCF课件以及The Laplacian Pyramid as a Compact Image Code by PETER J. BURT and EDWARD H. ADELSON）：代码实现如下：void myPyrUp(Ma

2017-08-24 13:49:13 1965

此篇文章是关于图像降采样的实现，主要是为了SIFT的实现做准备，侧重点是为了详细阐述降采样的实现，而无关乎优化，代码基于OpenCV 3.2.0&&C++实现。降采样算法主要采用高斯卷积实现，卷积核采用一维卷积核：double w[5] = {1.0/4 - a/2.0, 1.0/4, a, 1.0/4, 1.0/4 - a/2.0}，其中的a根据Matlab中降采样函数的说明，取a = 0.37

2017-08-21 23:01:26 19761 1

原创 Harris角点检测实现详解

本篇文章是对Harris角点检测基于OpenCV的C++实现总结与记录，程序代码力图实现最基本的算法步骤而无关优化，目的在于理解算法，关于Harris角点的基本原理，本篇不予赘述，网上搜索即可，下面只列出出自Computer Vision:Algorithm and Application的算法步骤：1, Compute the horizontal and vertical derivative

2017-08-21 00:51:43 913

Algorithms for Computer vision model learning Inference

This document accompanies the book \Computer vision: models, learning, and inference" by Simon J.D. Prince. It contains concise descriptions of almost all of the models and algorithms in the book. The goal is to provide sufficient information to implement a naive version of each method.

2017-08-19

Introduction to Machine Learning(Alpaydin,3rd ed,MIT Press,2014).pdf

Machine learning must be one of the fastest growing fields in computer science. It is not only that the data is continuously getting “bigger,” but also the theory to process it and turn it into knowledge. In various fields of science, from astronomy to biology, but also in everyday life, as digital technology increasingly infiltrates our daily existence, as our digital footprint deepens, more data is continuously generated and collected. Whether scientific or personal, data that just lies dormant passively is not of any use, and smart people have been finding ever new ways to make use of that data and turn it into a useful product or service. In this transformation, machine learning plays a larger and larger role.

2017-08-19

Machine Learning In Python

This book focuses on Python because it offers a good blend of functionality and specialized packages containing machine learning algorithms. Python is an often-used language that is well known for producing compact, readable code. That fact has led a number of leading companies to adopt Python for prototyping and deployment. Python developers are supported by a large community of fellow developers, development tools, extensions, and so forth. Python is widely used in industrial applications and in scientifc programming, as well. It has a number of packages that support computationally-intensive applications like machine learning, and it is a good collection of the leading machine learning algorithms (so you don’t have to code them yourself). Python is a better general-purpose programming language than specialized statistical languages such as R or SAS (Statistical Analysis System). Its collection of machine learning algorithms incorporates a number of top-ﬂight algorithms and continues to expand.

2017-08-19

USB下载接口程序

可用于单片机开发时下载程序，有win7，vistar，XP的操作系统的，内部有安装说明

2011-10-04

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原创 MnistData的读取

原创 BP神经网络的实现详解

原创图像升采样的实现详解

原创图像降采样的实现详解

原创 Harris角点检测实现详解

Algorithms for Computer vision model learning Inference

Introduction to Machine Learning(Alpaydin,3rd ed,MIT Press,2014).pdf

Machine Learning In Python

USB下载接口程序

空空如也

原创 MnistData的读取

原创 BP神经网络的实现详解

原创 图像升采样的实现详解

原创 图像降采样的实现详解

原创 Harris角点检测实现详解

Algorithms for Computer vision model learning Inference

Introduction to Machine Learning(Alpaydin,3rd ed,MIT Press,2014).pdf

Machine Learning In Python

USB下载接口程序

空空如也

原创图像升采样的实现详解

原创图像降采样的实现详解