文献名:Fast and accurate bacterial species identification in urine specimens using LC-MS/MS mass spectrometry and machine learning(利用质谱技术和机器学习模型在尿液样本中快速准确地进行菌种鉴定) doi: 10.1074/mcp.TIR119.001559 期刊名:Mol Cell Proteomics 作者:Florence Roux-Dalvai 通讯作者:Arnaud…
调试DeepFlow光流算法,由于作者给出的算法是基于Linux系统的,所以要在Windows上运行,不得不做大量的修改工作.移植到Windows平台,除了一些头文件找不到外,还有一些函数也找不到.这其中就涉及到三个函数:sgemv_,sgemm,saxpy_.百度了一下,原来这三个函数是很有来头的.它们仨来自于Basic Linear Algebra Subprograms(BLAS),即基础线性代数子程序库.这个库其实就是关于向量和矩阵之间的运算的. BLAS维百介绍:https://e…
Targeted learning methods build machine-learning-based estimators of parameters defined as features of the probability distribution of the data, while also providing influence-curve or bootstrap-based confidence internals. The theory offers a general…
1.What is machine learning Field of study that gives computers the ability to learn without being explicitly programmed A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performan…
by 南大周志华 摘要 监督学习技术通过学习大量训练数据来构建预测模型,其中每个训练样本都有其对应的真值输出.尽管现有的技术已经取得了巨大的成功,但值得注意的是,由于数据标注过程的高成本,很多任务很难获得如全部真值标签这样的强监督信息.因此,能够使用弱监督的机器学习技术是可取的.本文综述了弱监督学习的一些研究进展,主要关注三种弱监督类型:不完全监督,即只有一部分样本有标签:不确切监督,即训练样本只有粗粒度的标签:以及不准确监督,即给定的标签不一定总是真值. 关键词:机器学习,弱监督学习,监督学习…
源码:https://github.com/cheesezhe/Coursera-Machine-Learning-Exercise/tree/master/ex5 Introduction: In this exercise, you will implement regularized linear regression and use it to study models with different bias-variance properties. 1. Regularized Lin…
1.Matrices and vectors Matrix :Rectangular array of numbers a notation R3×3 Vector : An n×1 matrix this is a three dimensional vector , a notation R3 2.Addition and scalar multiplication 3.Matrix-vector multiplication 内标相同即可乘,前行×后列得一行 (可运用Python直接计算,…
发现自己不写总结真是件很恶劣的事情,好多学的东西没有自己总结都忘记了.所以决定从今天开始,学东西的时候一定跟上总结. 我写的东西大多数是自己通俗的总结,不太喜欢写严格的定义或者证明,写了也记不住,欢迎指正. 1. High Bias vs. High Variance High Bias:通常是因为模型过于简单,使得不能成功拟合数据.比如说一些有二次曲线特性的数据,如果用一次直线去拟合就会出现这个问题,所以它对应了Underfitting问题.另外,从泛化角度来说,这样的模型泛化程度更高. Hi…
1.Multiple features So what the form of the hypothesis should be ? For convenience, define x0=1 At this time, the parameter in the model is a ( + 1)-dimensional vector, and any training instance is also a ( + 1)-dimensional vector. The dimension of t…
1.Model representation Our Training Set [训练集]: We will start with this ''Housing price prediction'' example first of fitting linear functions, and we will build on this to eventually have more complex models 2.Cost function 代价函数(平方误差函数):It figures ou…
1.Non-linear Hypotheses 2.Neurons and the Brain 从某种意义上来说,如果我们能找出大脑的学习算法,然后在计算机上执行大脑学习算法或与之相似的算法,也许这将是我们向人工智能迈进做出的最好的尝试.人工智能的梦想就是:有一天能制造出真正的智能机器. 3.Model Representation I 第一层成为输入层(Input Layer),最后一 层称为输出层(Output Layer),中间一层成为隐藏层(Hidden Layers).我们为每一层都增…
1.The Problem of Over-fitting 2.Cost Function 3.Regularized Linear Regression 4.Regularized Logistic Regression import numpy as np def costReg(theta, X, y, learningRate): theta = np.matrix(theta) X = np.matrix(X) y = np.matrix(y) first = np.multiply(…
