udemy machine learning course

mathematical expression was unfolded using Taylor's formula, and looked a bit ugly, so we compared the Taylor expansion in the case of a one-dimensional argument.You know what's going on with the Taylor expansion in multidimensional situations.in the [1] type, the higher order infinitesimal can be ignored, so the [1] type is taken to the minimum value,should maketake the minimum-this is the dot product (quantity product) of two vectors, and in what case is the value minimal? look at the two vec

PrefaceThe Machine learning section records Some of the notes I have learned in the process of learning, including the online course or tutorial's study notes, the reading notes of the papers, the debugging of algorithmic code, the thinking of cutting-edge theory and so on, which will open different column series for d

classic paper; This book can be used as a supplementary reading for each of the two books. "Machine learning" (ml) PDFAuthor Tom Mitchell is a master of CMU, with a machine learning and semi-supervised learning Network course v

distribution with the mean value of μ 0 and the covariance matrix of Σ, X | y = 1 follows the multivariate Gaussian distribution where the mean value is μ1 and the covariance matrix is Σ (This will be discussed later ). The log function for maximum likelihood estimation is recorded as L (ø, μ 0, μ 1, Σ) = Log 1_mi = 1 p (x (I) | Y (I); μ 0, μ 1, Σ) P (Y (I); ø), our goal is to obtain the parameter ø, μ 0, μ 1, Σ to make L (ø, μ 0, 1, Σ) to obtain the maximum value. The values of the four para

Naive BayesianThis course outline:1. naive Bayesian- naive Bayesian event model2. Neural network (brief)3. Support Vector Machine (SVM) matting – Maximum interval classifierReview:1. Naive BayesA generation learning algorithm that models P (x|y).Example: Junk e-mail classificationWith the mail input stream as input, the output Y is {0,1},1 as spam, and 0 is not j

Reprint Please specify source: http://www.cnblogs.com/ymingjingr/p/4271742.htmlDirectory machine Learning Cornerstone Note When you can use machine learning (1) Machine learning Cornerstone Note 2--When you can use

Coursera Andrew Ng Machine learning is really too hot, recently had time to spend 20 days (3 hours a day or so) finally finished learning all the courses, summarized as follows:(1) Suitable for getting started, speaking the comparative basis, Andrew speaks great;(2) The exercise is relatively easy, but to carefully consider each English word, or easy to make mist

, i.e. gt,i=gt,i+n (0,σ2t) The variance of the Gaussian error requires annealing: σ2t=η (1+t) γ increasing the random error on the gradient increases the robustness of the model, even if the initial parameter values are not chosen well and is suitable for training in a particularly deep-seated network. The reason for this is that increasing random noise is more likely to jump over local extreme points and find a better local extremum, which is more common in deep networks. Summary in the above

Prismatic: using machine learning to analyze user interests takes 10 seconds [Date: 2013-01-03] Source: csdn Author: Todd Hoff [Font: large, medium, and small] Http://www.chinacloud.cn/show.aspx? Id = 11857 cid = 17 About prismaticFirst, there are several things to explain. Their entrepreneurial team is small,OnlyComposed of four computer scientistsThree of them are young

in fact, Machine Learning has been addressing a variety of important issues. For example , in the mid-decade, people have begun to use neural networks to scan credit card transactions to find fraudulent behavior; at the end of the year,Google Use this technology for Web search. but at that time, machine learning was n

Original address: Ten machine learning Examples in JavaScriptIn the past year, Libraries for machine learning (machines learning) have become increasingly fast and easy to use. Python has always been the language of choice for machine