tensorflow_cpu2.5.0版本3.7whl tensorflow2.5.0 py3.7whl文件 pip install tensorflow 即可安装文件
2021-06-21 18:05:44 402.99MB python 人工智能 tensorflow 自然语言处理
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无加密资源,python 未来趋势,多掌握一个技能吧
2021-06-14 13:01:43 115B python 人工智能
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用 Python 实现一组手写数字识别。使用keras+opencv进行简单的实现。首先进行图像中数字的目标检测与分割,将图片中的数字分离出来然后进行单独识别。使用的数据集为mnist手写数字识别库,采用卷积神经网络进行识别
2021-06-13 20:08:40 14.44MB python 人工智能 opencv
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python实现八数码问题,代码可读性较好
2021-06-05 13:05:59 13KB python 人工智能
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人工智能实验2、3
2021-06-05 13:05:59 1.43MB python 人工智能
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学弟们加油!这是学长之前花了好久的时间写的,第1、3个实验效果还可以,第2个实验可能有点缺陷,因为写1、3实验前我每个版本都写思路了,但第2了没写,所以有点逻辑混乱,此外,我第一个实验用遗传算法解决01背包问题选择算法用的截断选择不太好,所以精英池没有设置很大,你们改进一下吧,好像第一个实验最佳的结果是2500左右,我的只有2200左右,不过还行吧,毕竟是新手之前还不会python做到这样很不错了
2021-06-02 15:42:13 472KB python 人工智能 01背包 PCA贝叶斯分类
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用python写的一段贝叶斯网络的程序 This file describes a Bayes Net Toolkit that we will refer to now as BNT. This version is 0.1. Let's consider this code an "alpha" version that contains some useful functionality, but is not complete, and is not a ready-to-use "application". The purpose of the toolkit is to facilitate creating experimental Bayes nets that analyze sequences of events. The toolkit provides code to help with the following: (a) creating Bayes nets. There are three classes of nodes defined, and to construct a Bayes net, you can write code that calls the constructors of these classes, and then you can create links among them. (b) displaying Bayes nets. There is code to create new windows and to draw Bayes nets in them. This includes drawing the nodes, the arcs, the labels, and various properties of nodes. (c) propagating a-posteriori probabilities. When one node's probability changes, the posterior probabilities of nodes downstream from it may need to change, too, depending on firing thresholds, etc. There is code in the toolkit to support that. (d) simulating events ("playing" event sequences) and having the Bayes net respond to them. This functionality is split over several files. Here are the files and the functionality that they represent. BayesNetNode.py: class definition for the basic node in a Bayes net. BayesUpdating.py: computing the a-posteriori probability of a node given the probabilities of its parents. InputNode.py: class definition for "input nodes". InputNode is a subclass of BayesNetNode. Input nodes have special features that allow them to recognize evidence items (using regular-expression pattern matching of the string descriptions of events). OutputNode.py: class definition for "output nodes". OutputBode is a subclass of BayesNetNode. An output node can have a list of actions to be performed when the node's posterior probability exceeds a threshold ReadWriteSigmaFiles.py: Functionality for loading and saving Bayes nets
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This assignment is a modified version of the Driverless Car assignment written by Chris Piech. A study by the World Health Organization found that road accidents kill a shocking 1.24 million people a year worldwide. In response, there has been great interest in developing autonomous driving technology that can drive with calculated precision and reduce this death toll. Building an autonomous driving system is an incredibly complex endeavor. In this assignment, you will focus on the sensing system, which allows us to track other cars based on noisy sensor readings.
2021-05-30 00:10:15 182KB python 人工智能
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Age-DB, 收集了一些不同年龄的人脸,可以用来做相关的测试。
2021-05-29 17:34:10 172.52MB age-db python 人工智能 深度学习
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视频内容见http://bbs.vxia.net/thread-1148-1-1.html 麦子学院特价:2999元
2021-05-16 15:09:38 59B python 人工智能 视频课程 百度云
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