QA Official

MyBatis Learning 2019-04-15
From: 1. Introduction and Configuration of MyBatis MyBatis+Spring+MySql 1.1 Brief Introduction of MyBatis MyBatis is a persistence layer framework that can customize SQL, stored procedures, and advanced mappings.MyBatis excluded most JDBC code, manually set parameters and retrieved result sets.MyBatis only uses simple XML and annotations to configure and Map basic data types, map and POJO to database records.Compared with "one-stop" ORM solutions such as Hibernate and ApacheOJB, Mybatis is

Openface (4): load data 2019-04-15
recently learned the Openface, an open source face recognition program.In the previous articles in inside, we aligned the faces and established a convolution network of face recognition Facenet using the Inception module.In this article, we realized the loading of data before neural network training.Pytorch specially provides loading tools: DataLoader, the parameters of which are torch.utils.dataset, and torch.utils.dataset provide two methods, one is to read images and the other is to

R language -data.table package (convenient for reference) 2019-04-15
R语言-data.table包 它的fread函数读取1G的CSV文件才用了20s左右。其他对data.frame的操作,也快了N倍 特点 dat

git Learning, git Video Tutorial, git Data Sharing 2019-04-15
Git learning, Git video tutorial, here the personal learning experience is summarized as follows:Linus's version control project for Linux Kernel Project. HEAD represents the current latest status. tag is a tag of a state. SHA1 (silly one) is the unique identification of each submission log. install: apt-get install git-core git clone: git warehouse can be obtained using git clone:

opencv Computer Vision Learning Notes 8 2019-04-15
Chapter 9 Introduction of Neural Networks Based on opencv 1 artificial neural network ann 2 Structure of Artificial Spirit Network input layer Number of network inputs If the animal has three attributes of weight, length and teeth, the network needs three input nodes. middle layer output layer Same number of categories as defined. If pigs, dogs, cats and chickens are defined, the number of output layers is 4 Create ANN

openface configured under liunx 2019-04-15
openface will be updated again in 2015, so you can refer to the previous steps when configuring openface again. Only dlib is partially modified here, and Operating instructions of demo1 is also modified to some extent.Original blog: article mainly refers to the website of openface:'s website: the same time, I would like to thank the author of openface, bamos, for his guidance during configuration. I would like to thank the original author.

ubuntu16.04.3 Setting up open face (1) 2019-04-15
1, Openface environment construction System: Ubuntu 14.04 64-bit Desktop Operating SystemReference: Ubuntu Switch root User Not detailed here, if you want to use ordinary users, please test yourself.Reference articles: 2, preparation before installation Install the necessary programs, either in the following batch or one by one.sudo apt-get install build-essential -ysudo apt-get install cmake -ysudo apt-get install curl -ysudo apt-get install gfortran -ysudo apt-get install git -ysudo apt-get install libatlas-dev -ysudo apt-get install libavcodec-dev -ysudo apt-get install libavformat-dev -ysudo apt-get install libboost-all-dev -ysudo apt-get install libgtk2.

Addition, deletion, modification and check of student information-inquiry module 2019-04-15
general idea: query the data from the database and display it on the page to form a list. query requires a result set: resultsets = null; To query, you need to traverse Information Query Service Processing Combines: 1. Write a query page 2. Receive query condition information in servlet 3. Processing Query SQL Statements in DAO 4. Return data (conditional results) to the page

C++:Implementation of Vector and List 2019-04-15
Implementation of Vector //test.h #pragma once #include <iostream> #include <cstdio> #include <string.h> #include <assert.h> using namespace std; typedef int DataType; #define TESTHEADER printf("\n================%s===============\n", __FUNCTION__) class Vector { public: Vector(); Vector(const Vector& v); ~Vector(); Vector& operator = (Vector& v); void Swap(Vector& v); size_t Size()const; size_t Capacity()const; void Reserve(size_t n); void Resize(size_t n, DataType); DataType& operator[](size_t pos); void PushBack(DataType v); void PopBack(); private: void Expand(size_t n); DataType* _start; DataType* _finish; DataType* _endofstorage;

Cross Validation of Python Machine Learning 2019-04-15
Data Link: Password: ejki import numpy as np import matplotlib.pyplot as plt import pandas as pd import seaborn as sns from sklearn.model_selection import train_test_split 1, data loading # 加载数据集 fruits_df = pd.read_table('fruit_data_with_colors.txt') print(fruits_df.head()) print('样本个数:', len(fruits_df)) # 创建目标标签和名称的字典 fruit_name_dict = dict(zip(fruits_df['fruit_label'], fruits_df['fruit_name']))