clojure machine learning

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

Deep Learning SpecializationWunda recently launched a series of courses on deep learning in Coursera with Deeplearning.ai, which is more practical compared to the previous machine learning course. The operating language also has MATLAB changed to Python to be more fit to the current trend. A study note on this series o

In fact, there are many ways to learn about machine learning and many resources such as books and open classes. Some related competitions and tools are also a good helper for you to understand this field. This article will focus on this topic, give some summative understanding, and provide some learning guidance for the transformation from programmers to

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

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

=ss_x.fit_transform (x_train) x_test=ss_x.transform (x_test) ss_y=Standardscaler () Y_train= Ss_y.fit_transform (Y_train.reshape (-1, 1)) Y_test= Ss_y.transform (Y_test.reshape (-1, 1))#4.1 Support vector machine model for learning and prediction#linear kernel function configuration support vector machineLinear_svr = SVR (kernel="Linear")#TrainingLinear_svr.fit (X_train, Y_train)#forecast Save Forecast resu

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

July Algorithm-December machine Learning online Class -12th lesson note-Support vector machine (SVM) July algorithm (julyedu.com) December machine Learning Online class study note http://www.julyedu.com?What to review: Duality problem KKT conditions? SVM1.1

Deep understanding of Java Virtual Machine-learning notes and deep understanding of Java Virtual Machine JVM Memory Model and partition JVM memory is divided: 1.Method Area: A thread-shared area that stores data such as class information, constants, static variables, and Code Compiled by the real-time compiler loaded by virtual machines. 2.Heap:The thread-shared

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

linear kernel)The neural network works well in all kinds of n, m cases, and the defect is that the training speed is slow.Reference documents[1] Andrew Ng Coursera public class seventh week[2] Kernel Functions for machine learning applications. http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applicati

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

Do not say anything, actual combat Java Virtual Machine, good study, Day day up! Develop a learning plan for your own weaknesses.Part of the content to read, do their own study notes and feelings.Java is very simple to learn, but it is difficult to understand Java, if your salary is not more than 1W, it is time to go deep into the study suddenly.5 Notes while learning