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Kares入门资料打包
深度学习框架Keras入门资料，里面的代码包括课件和DEMO有利于新书入门学习，简单易懂(Keras Introductory Information of Deep Learning Framework, which includes courseware and DEMO, is helpful for introductory learning of new books. It is easy to understand.)
- 2020-06-17 17:00:01下载
- 积分：1
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机器学习决策树2个经典案例
机器学习决策树
- 2019-05-10下载
- 积分：1
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test
说明： 使用Python语言，基于逻辑回归的算法，实现房价的预测(predict housing price)
- 2020-06-17 01:20:01下载
- 积分：1
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faceRecognition
说明： 该demo展示了使用opencv和face_recognition库，对图片中出现的人脸识别记录并框出的过程，可实际应用(This is a demo which implements face recognition with opencv and face_recognition libraries)
- 2020-06-24 09:40:07下载
- 积分：1
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3D-R2N2-master
说明： 单图像和多图像三维重建，基于theano包的一个重建代码，够20个子了吗(3d reconstruction from both single and muti image)
- 2021-05-01 11:01:52下载
- 积分：1
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OFDM信道估计仿真
说明： OFDM系统仿真内容，比较简单的说明，希望有用哦。(OFDM Channel estimation)
- 2021-03-03 16:13:08下载
- 积分：1
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joint_sparse_algorithms-master
说明： 我们描述了所提出的方法对超声（US）信号的压缩多路复用的直接应用。该技术利用压缩多路复用器架构进行信号压缩，并依靠频域中US信号的联合稀疏性进行信号重建。由于换能器元件具有压电特性，因此可以获得有关US信号频率支持的准确先验知识，并且可以在联合稀疏算法中使用。
我们在数值实验中验证了所提出的方法，并显示了它们在秩次缺陷情况下相对于最新方法的优越性。我们还证明，与没有已知支持的重建相比，该技术可显着提高体内颈动脉图像的图像质量。(We describe a direct application of the proposed methods for compressive multiplexing of ultrasound (US) signals. The technique exploits the compressive multiplexer architecture for signal compression and relies on joint-sparsity of US signals in the frequency domain for signal reconstruction. Due to piezo-electric properties of transducer elements, accurate prior knowledge of the frequency support of US signals is available and can be used in joint-sparse algorithms.
We validate the proposed methods on numerical experiments and show their superiority against state-of-the-art approaches in rank-defective cases. We also demonstrate that the techniques lead to a significant increase of the image quality on in vivo carotid images compared to reconstruction without known support.)
- 2020-03-16 16:45:38下载
- 积分：1
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shibie
说明： c4.5实现手写数字体识别，手写数字识别，数据集使用的是自建手写体库(Realization of handwritten numeral recognition with C4.5)
- 2020-11-12 08:52:25下载
- 积分：1
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Python神经网络编程.pdf+代码
本书首先从简单的思路着手，详细介绍了理解神经网络如何工作所必须的基础知识。第一部分介绍基本的思路，包括神经网络底层的数学知识，第2部分是实践，介绍了学习Python编程的流行和轻松的方法，从而逐渐使用该语言构建神经网络，以能够识别人类手写的字母，特别是让其像专家所开发的网络那样地工作。第3部分是扩展，介绍如何将神经网络的性能提升到工业应用的层级，甚至让其在Raspberry Pi上工作。(This book begins with a brief introduction to the basics necessary to understand how neural networks work. The first part introduces the basic ideas, including the basic mathematical knowledge of the neural network, the second part is the practice, introduces the popular and easy way to learn Python programming, so as to gradually use the language to construct neural networks to recognize human handwritten letters, especially to make them as well as the network developed by experts. Do. Part 3 is an extension of how to improve the performance of neural networks to the level of industrial applications, and even let them work on Raspberry Pi.)
- 2018-10-16 11:05:31下载
- 积分：1
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dispatch
gurobi python optimization programing
- 2019-04-16 18:27:38下载
- 积分：1
