## 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*