1. The complete course of statistics all of statistics Carnegie Kimelon Wosseman
2. Fourth edition, "Probability Theory and Mathematical Statistics" Morris. Heidegger, Morris H.degroot, and Mark. Schevish (Mark j.shervish)
3. Introduction to Linear algebra, Gilbert. Strong--Online video tutorials are classic
4. "Numerical linear algebra", Tracy Füssen. Lloyd and David. Bao
Textbooks suitable for undergraduates
5. Predictive data analysis of machine
Analytical:Two categories: Each classifier can only divide the samples into two categories. The prison samples were warders, thieves, food-delivery officers, and others. Two classifications certainly won't work. Vapnik 95 proposed to the basis of the support vector machine is a two classification classifier, this classifier learning process is to solve a positive and negative two classification derived fro
Original address: http://www.cnblogs.com/cyruszhu/p/5496913.htmlDo not use for commercial use without permission! For related requests, please contact the author: [Email protected]Reproduced please attach the original link, thank you.1 BasicsL Andrew NG's machine learning video.Connection: homepage, material.L 2.2008-year Andrew Ng CS229 machine LearningOf course
We have developed a false news detector using machine learning and natural language processing, which has an accuracy rate of more than 95% on the validation set. In the real world, the accuracy rate should be lower than 95%, especially with the passage of time, the way the creation of false news will change.
Because of the rapid development of natural language processing and
Nonlinear Transformation (nonlinear conversion)
ReviewIn the 11th lecture, we introduce how to deal with two classification problems through logistic regression, and how to solve multiple classification problems by Ova/ovo decomposition.
Quadratic hypothesesThe two-time hypothetical space linear hypothetical space is extremely flawed:
So far, the machine learning model we have introduced is linear model,
is all 0. And because it can be deduced that b=1nz∗zt=wt∗ (1NX∗XT) w=wt∗c∗w, this expression actually means that the function of the linear transformation matrix W in the PCA algorithm is to diagonalization the original covariance matrix C. Because diagonalization in linear algebra is obtained by solving eigenvalue and corresponding eigenvector, the process of PCA algorithm can be introduced (the process is mainly excerpted from Zhou Zhihua's "machine
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
first, gradient descent method
In the machine learning algorithm, for many supervised learning models, the loss function of the original model needs to be constructed, then the loss function is optimized by the optimization algorithm in order to find the optimal parameter. In the optimization algorithm of machine
http://blog.csdn.net/pipisorry/article/details/44119187Machine learning machines Learning-andrew NG Courses Study notesMachine Learning System DesignPrioritizing what do I do on priorityError analysisError Metrics for skewed Classes Error metrics with biased classesTrading Off Precision and recall weigh accuracy and recall rateData for machines
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
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
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
Reprint Please specify the Source: http://www.cnblogs.com/ymingjingr/p/4271742.htmlDirectoryMachine learning Cornerstone Note When machine learning can be used (1)Machine learning Cornerstone Note 2--When you can use machine
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
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
Experimental purposes
Recently intend to systematically start learning machine learning, bought a few books, but also find a lot of practicing things, this series is a record of their learning process, from the most basic KNN algorithm began; experiment Introduction
Language: Python
GitHub Address: LUUUYI/KNNExperiment
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
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.
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
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