three-dimensional-graphics

最新：先进PID控制及其MATLAB仿真（第二版），附带书本例子源码，值得学习！(Latest: advanced PID control and MATLAB (second edition), with examples of source books, it is worth learning!)

Equalizer, audioplayer, GUI

深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)

It is a files related to basic programes of MATLAB

The book is based on machines reference frame theory it gives clear explanations......

掌纹识别预处理特征提取和算法的研究，网上借鉴的(palmprint recognition。to design 2d gabor filter and apply it to image....we get a filter characteristics closely resembling to the gabor filters. the gabor filter is basically a gaussian )

基于matlab图像处理的GUI设计，里面有程序和报告设计(GUI design based on MATLAB image processing)

ford-fulkerson算法，通过标号寻找网络最大流(ford-fulkerson algorithm )

详细介绍了matlab中的ODE函数求解偏微分方程组的各种方法，附带示例，适合初学者学习。(Details of the ODE function matlab various methods for solving partial differential equations, with examples, for beginners to learn.)

mppt_incc is incremental conductance