JD sliding verification code cracking of selenium and PIL
Single layer graph slide verification code of selenium and PIL
How to crack the slide verification code
preparation in advance
Code operation
How to crack the slide verification code
There are various mechanisms of anti climbing on the market, among which the sliding verification code is divided into two-layer graph ...
Posted by Kev on Sun, 10 May 2020 01:54:26 -0700
Machine learning Chapter 4 training model
Reference: author's Jupyter NotebookChapter 2 – End-to-end Machine Learning project
Generate picture and savefrom __future__ import division, print_function, unicode_literals
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import os
np.random.seed(42)
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.r ...
Posted by Scriptor on Fri, 27 Mar 2020 03:26:21 -0700
Naive Bayesian understanding and code reproduction of statistical learning methods
Naive Bayes
The joint probability P(A,B) = P(B|A)*P(A) = P(A|B)*P(B) combines the two equations on the right to get the following formula:
P (a| b) indicates the probability of a in the case of B. P(A|B) = [P(B|A)*P(A)] / P(B)
Let's have A visual understanding of this formula. As shown in the figure ...
Posted by PHP_apprentice on Sun, 15 Mar 2020 22:13:16 -0700
NO.89 - Applying Xgboost to Predict Insurance Compensation
Article Directory
1 Data analysis
1.1 First look at what the data looks like
1.2 Continuous and categorical variables
1.3 Number of attributes in categorical variables
1.4 Compensation value
1.5 Continuous Variable Characteristics
1.6 Correlation between features
2 Xgboost
2.1 Data Preprocessing
2.2 ...
Posted by rimelta on Wed, 11 Mar 2020 21:00:47 -0700
"Python drawing sharp tool": Matplotlib tutorial
Article directory
1. Preface
2. Introduction to Matplotlib
3. installation
4. Drawing basis
4.1 plot() function
4.1.1 Chinese display
4.1.2 text display
5. Drawing of common charts
5.1 histogram
5.1.1 simple example
5.1.2 function definition
5.1.3 histogram of normal distribution
5.2 pie chart ...
Posted by BLaZuRE on Thu, 05 Mar 2020 19:40:13 -0800
Python infrastructure
When building a house, the choice of wood is a problem.
A carpenter's goal is essentially to carry a good cutting tool. When he has time, he sharpens his equipment.
{-:} - Miyamoto Musashi (wulunshu)
This article is excerpted from Chapter 2 of Python big data analysis (version 2)
For new Python users, ...
Posted by jmurch on Wed, 26 Feb 2020 19:11:26 -0800
Splitting and Extracting Text Data in pandas
This paper mainly shares the splitting, extracting and merging of text data to prepare for the next visual analysis.
The data comes from the employment information of boss and dragnet data analysis positions, totaling 9458.
The crawling methods of the pull-hook are as follows: Python selenium+beautifuls ...
Posted by timelf123 on Mon, 24 Feb 2020 19:58:24 -0800
The application of Matplotlib in solving ordinary differential equation of Python 3 SCI
Python scientific calculation simply records several notes, uses SciPy to solve ordinary differential equations, and uses matplotlib to demonstrate in jupyter notebook. The following points need to be noted:
odeint function provided by integration module
On jupyter notebook of Anaconda 3
matplotlib 2D drawing to solve Newton's cooli ...
Posted by phpcodec on Thu, 13 Feb 2020 12:52:36 -0800
Docker build container build acceleration strategy
It takes a lot of time to download many kinds of software when building containers. hub.docker.com is slow in nature, especially when it encounters modules stored on gcr.io/aws and so on. pip installation of python module is also slow, and the download of conda is like a snail.
There are several ways to speed up the download of container const ...
Posted by safrica on Thu, 30 Jan 2020 07:19:13 -0800
Advanced Neural Network for Python Deep Learning Experiment
Advanced Neural Network
Experimental environment
keras 2.1.5
tensor 1.4.0
Experimental tools
Jupyter Notebook
Experiment 1: MNIST generates antagonistic networks
thinking
Train two models, one to generate a given random noise as input output example G, and one to identify the generated m ...
Posted by jonat8 on Sat, 11 Jan 2020 16:58:06 -0800