top machine learning books

There is a period of time does not dry goods, home are to be the weekly lyrics occupied, do not write anything to become salted fish. Get to the point. The goal of this tutorial is obvious: practice. Further, when you learn some knowledge about machine learning, how to deepen the understanding of the content through practice. Here, we make an example from the 2nd-part perceptron of Dr. Hangyuan Li's statist

Use Python to implement machine awareness (python Machine Learning 1 ).0x01 Sensor A sensor is a linear classifier of the second-class Classification and belongs to a discriminant model (another is to generate a model ). Simply put, the objective is divided into two categories by using the input feature and the hyperplane. Sensor machines are the foundation of ne

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

Self-study machine learning three months, exposure to a variety of algorithms, but many know its why, so want to learn from the past to do a summary, the series of articles will not have too much algorithm derivation.We know that the earlier classification model-Perceptron (1957) is a linear classification model of class Two classification, and is the basis of later neural networks and support vector machin

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

　　Statement: This blog post according to Http://www.ctocio.com/hotnews/15919.html collation, the original author Zhang Meng, respect for the original.Machine learning is undoubtedly a hot topic in the field of current data analysis. Many people use machine learning algorithms more or less in their usual work. This article summarizes common

decision trees (decision tree) 4 　　Cited examplesThe existing training set is as follows, please train a decision tree model to predict the future watermelon's merits and demerits.Back to Catalog What are decision trees (decision tree) 5 　　Cited examplesThe existing training set is as follows, please train a decision tree model to predict the future watermelon's merits and demerits.Back to Catalog What are decision trees (decision tree) 6

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

Perception Machine (Perceptron)The Perceptron (Perceptron) was proposed by Rosenblatt in 1957 and is the basis of neural networks and support vector machines. Perceptron is a linear classification model of class Two classification, its input is the characteristic vector of the instance, the output is the class of the instance, and the value of +1 and 12 is taken. The perceptual machine corresponds to the se

both.Jieba-Chinese word breaker toolSNOWNLP-Chinese Text Processing libraryLoso-Another Chinese word-breaking libraryGenius-Chinese word-breaking database based on conditional random domainNut-Natural Language Understanding ToolkitGeneral Machine LearningBayesian Methods for Hackerse-Book for-python language probabilistic programmingMLlib in Apache SparkDistributed machine

machine learning is divided into two types: supervised learning and unsupervised learning . Next I'll give you a detailed introduction to the concepts and differences between the two methods. Supervised Learning (supervised learning