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machineLearning
机器学习-分类器-垃圾邮件系统。数据集来自UCI-spambase.data.(Machine Learning- spam classifier.)
- 2021-04-16 09:18:53下载
- 积分：1
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MAgent-master
多智能体的一段代码，有关强化学习，机器学习，很实用的一段代码！(A code of multi-agent, about reinforcement learning, machine learning, a very practical piece of code!)
- 2020-10-20 20:47:26下载
- 积分：1
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AVA_dataset
ava数据库下载代码,用于图像美学质量评价(download ava dataset，image aesthetic assessment)
- 2017-04-15 20:13:00下载
- 积分：1
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Python面试大全（245道面试题）
Python面试大全（245道面试题）
- 2020-12-09下载
- 积分：1
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python jd模拟登陆，优惠卷领取
需要python3.6环境，使用时需安装redis,在setting.py 设置 redis 地址，运行run.py,用过input_tool.py 控制操作。
- 2023-06-24 15:45:03下载
- 积分：1
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Panda3D17
Packt - Panda3D 1.7 Game Development Cookbook 2011 RETAiL eBOOk(Packt- Panda3D 1.7 Game Development Cookbook 2011 RETAiL eBOOk)
- 2011-06-27 03:02:55下载
- 积分：1
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Tensorflow-Deep-Neural-Networks-master
说明： DBN源码(DBN source code)
- 2019-05-25 16:40:14下载
- 积分：1
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winWord2
生成以日期为文件名的word文档，写入操作(creating word document named as date)
- 2015-08-04 20:58:41下载
- 积分：1
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fenlei
说明： 利用深度学习进行遥感图像场景分类
这里我们对NWPU-RESISC45数据集的场景图像进行分类
我们将卷积神经网络应用于图像分类。我们从头开始训练数据集。此外，还应用了预先训练的VGG16 abd ResNet50进行迁移学习。(Scene Classification of Remote Sensing Images Using Deep Learning
Here we classify scene images from NWPU-RESISC45 dataset
We apply convolutional neural network to image classification. We start training data sets from scratch. In addition, a pre-trained VGG16 abd ResNet50 is used for migration learning.)
- 2021-03-31 20:19:08下载
- 积分：1
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svm解析验证码
SVM做的验证码识别，准确率85%# -*- coding: utf-8 -*
__author__ = "eple"
from svmutil import *
from PIL import Image
from numpy import *
import os
import string
def getFeatures(path):
features = []
im = array(Image.open(path).convert("L"))
h,w = shape(im)
for i in xrange(0,h):
for j in xrange(0,w):
im[i][j] = 0 if im[i][j] < 128 else 1
for i in xrange(1,h-1):
for j in xrange(1,w-1):
if im[i][j] != 0 and im[i-1][j]==0 and im[i][j-1]==0 and im[i][j+1]==0 and im[i+1][j]==0:
im[i][j] = 0
for rang in [xrange(2,8), xrange(12,18), xrange(22,28), xrange(32,38)]:
temp = []
for i in rang:
for j in xrange(0,10):
temp.append(im[j][i])
features.append(temp)
return features
- 2022-04-14 17:07:19下载
- 积分：1
