Gradient descent algorithm minimization of cost function J gradient descent
Using the whole machine learning minimization first look at the General J () function problem
We have J (θ0,θ1) we want to get min J (θ0,θ1) gradient drop for more general functions
J (Θ0,θ1,θ2 .....) θn) min J (θ0,θ1,θ2 .....) Θn) How this algorithm works. : Starting from the initial assumption
Starting from 0, 0 (or any other valu
new feature $f$ given the $x$ of a data point. When $\THETA^TF \geq 0$, predict $y=1$, and conversely, predict $y=0$.Training (Training): $$\min\limits_\theta c\left[\sum\limits_{i=1}^{m}y^{(i)}cost_1 (\theta^tf^{(i)}) + (1-y^{(i)}) Cost_0 ( \theta^tf^{(i)}) \right] + \frac{1}{2}\sum\limits_{j=1}^{n}\theta_{j}^2$$Effect of parameter C ($\approx\frac{1}{\lambda}$):
Large c:low bias, high variance
Small c:high bias, low variance
Effect of parameter $\sigma^2$:
Large $\s
http://blog.csdn.net/zhangyingchengqi/article/details/50969064First, machine learning1. Includes nearly 400 datasets of different sizes and types for classification, regression, clustering, and referral system tasks. The data set list is located at:http://archive.ics.uci.edu/ml/2. Kaggle datasets, Kagle data sets for various competitionsHttps://www.kaggle.com/competitions3.Second, computer vision"Machine
a good effect, basically do not know what method of time can first try random forest.SVM (Support vector machine)
The core idea of SVM is to find the interface between different categories, so that the two types of samples as far as possible on both sides of the surface, and the separation of the interface as much as possible.
The earliest SVM was planar and limited in size. But using the kernel function (kernel functions), we can make the plane proj
Which programming language should I choose for machine learning ?, Machine Programming Language
Which programming language should developers learn to get jobs like machine learning or data science?
This is a very important issue. We have discussed this issue in many forums.
Definition of successive descent method:
For a given set of equations, use the formula:where k is the number of iterations (k=0,1,2,... )The method of finding approximate solution by stepwise generation is called iterative method
If it exists (recorded as), it is said that this iterative method converges, obviously is the solution of the equations, otherwise called this iterative method divergence.
Study the convergence of {}. Introducing Error Vectors:Get:Recursion gets:To inve
This article is from: http://blog.jobbole.com/56256/This is a hard-to-write article because I hope this article will inspire learners. I sat down in front of the blank page and asked myself a difficult question: what libraries, courses, papers, and books are best for beginners in machine learning.It really bothers me how to write and write nothing in the article. I have to think of myself as a programmer and a beginner of
I. Working methods of machine learning
① Select data: Divide your data into three groups: training data, validating data, and testing data
② model data: Using training data to build models using related features
③ validation Model: Using your validation data to access your model
④ Test Model: Use your test data to check the performance of the validated model
⑤ Use model: Use fully trained models to mak
;Machine Learning:an Algorithmic Perspective (2nd ed.), Stephen Marsland, 2015;Deep Learning, an online book;Neural Networks and Learning Machines (3rd ed.), Simon O. Haykin, 2008, with Chinese translation: Neural Network and machine learning;Pattern recognition and
Octave Machine Learning Common commands
A, Basic operations and moving data around
1. Attach the next line of output with SHIFT + RETURN in command line mode
2. The length command returns a higher one-dimensional dimension when apply to the matrix
3. Help + command is a brief aid for displaying commands
4. doc + command is a detailed help document for displaying commands
5. Who command displays all current
This paper is organized from the "machine learning combat" and Http://write.blog.csdn.net/posteditBasic Principles of Mathematics:
Very simply, the Bayes formula:
Base of thought:
For an object to be sorted x, the probability that the thing belongs to each category Y1,y2, which is the most probability, think that the thing belongs to which category.Algorithm process:
1. Suppose something to be sorted x, it
:
, where θ is the vector of (n+1) x1, x is the vector of (n+1) x1, ∙.
We all use vectors to represent the hyper-plane behind.
Except that θ is called a weight, and b is biased, so the complete expression of the super plane is:θ*x+b=0
The Perceptron model can be defined as y=sign (θ∙x+b) where:
If we call sign the activation function, the difference between the perceptual machine and the logistic regression is that the sign,logistic regression acti
Brief introduction:Support Vector Machine (SVM) is a supervised learning model of two classification, and his basic model is a linear model that defines the largest interval in the feature space. The difference between him and the Perceptron is that the perceptron simply finds the hyper-plane that can divide the data correctly, and SVM needs to find the most spaced hyper-plane to divide the data. So the per
), network analysis and canvas visualization.
The pattern, produced by the clips Laboratory at the University of Antwerp in Belgium, objectively says that pattern is not just a set of text processing tools, it is a Web data mining tool that includes data capture modules (including Google, Twitter, Wikipedia APIs, As well as crawlers and HTML analyzers), Text processing modules (part-of-speech tagging, sentiment analysis, etc.), machine
Tags: virtual machine installation
Connect to the Linux virtual machine learning environment Build-Virtual machine Create "click" to open this virtual machine, enter the system installation interface.650) this.width=650; "Src=" Https://s1.51cto.com/oss/201711/17/0f55f83d
The topic of machine learning techniques under this column (machine learning) is a personal learning experience and notes on the Machine Learning Techniques (2015) of Coursera public co
Python machine learning Chinese version, python machine Chinese Version
Introduction to Python Machine Learning
Chapter 1 Let computers learn from data
Convert data into knowledge
Three types of machine
Machine learning DefinitionMachine learning is a branch of AI that aims to give machines a new ability. (specialized in how computers simulate or implement human learning behaviors in order to acquire new knowledge or skills and reorganize existing knowledge structures to continually improve their performance.)
Types of learning according to my personal understanding, the classification of learning methods in machine learning helps us face a specific problem, you can select an appropriate machine learning algorithm based on your goals. F
Support Vector MachineSVM (Support vector Machines,svms) is a two-class classification model. Its basic model is a linear classifier that defines the largest interval in the feature space, which distinguishes it from the perceptual machine, and the support vector machine also includes the kernel technique, which makes it a substantial nonlinear classifier. The learning
The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion;
products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the
content of the page makes you feel confusing, please write us an email, we will handle the problem
within 5 days after receiving your email.
If you find any instances of plagiarism from the community, please send an email to:
info-contact@alibabacloud.com
and provide relevant evidence. A staff member will contact you within 5 working days.