best machine learning book

Part I: ClassificationThe first two parts of the book focus on supervised Learning (supervisedieaming). In the process of supervising learning, we only need to give the input sample set , and the machine can push the possible results of the specified target variable from it. Supervised

Starting from February 16, 2016, I wrote an article every day to record my learning experiences. I started my article "how to read a book-Experiences", which will begin in 2016. How to read a book-Experiences -- February 12, 2016 To achieve all the purposes of reading, you must use appropriate speed when reading different books. Reading is too fast or too slow, a

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

more than 700, the length of the second version of the increase of nearly two-thirds, the book content richer. Finally, on the basis of the second edition, we also add the content that is not involved in the textbook of similar standard type theory, linear mapping and generalized inverse of matrices based on elementary factors in general number field.Different from the general higher algebra problem Sets or postgraduate study guidance books, the

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

habit.The third analytic hierarchy of reading rules one: The importance of the classification of books; Rule number two: Use a single sentence, or up to a few words (a short paragraph of text) to describe the whole book; rule three: Enumerate the important chapters of the book and explain how they form a whole structure in order.Fourth level topic reading node: ① Browse the relevant chapters of the

patterns, beginners recommended; "tao nature" is a very good book written by the Chinese people, with their own written framework system as an example, throughout the full text, explain the use of various patterns. After looking at it, I realized that the application of the model in large projects, especially in this framework system, was really in place. At the same time, familiar with the MFC friends will be in this

In this case, C ++ has been around for year 78, and there is something that can be shared; Most of the books and wooden bookshelves recommended below are in electronic versions. For technical books, the electronic version does not bring a good reading experience. If you like it, you 'd better find the paper version (the Library is a good place). The electronic version is more suitable for use as a reference book standing computer. I will introduce

images in Python, which has a pretty good effect. SVG chart builder in pygal-Python. Pycascading Miscellaneous scripts/ipython notes/code library Pattern_classification Thinking stats 2 Hyperopt Numpic 2012-paper-diginorm Ipython-notebooks Demo-weights Sarah Palin lda-Sarah Palin's email about topic modeling. Diffusion segmentation-a set of image segmentation algorithms based on the diffusion method. Scipy tutorials-scipy tutorial. It is out of date. Please refer to scipy-lecture-n

: , 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

complete, a default offline address book is generated, which is typically used in this address book. However, you need to create a new offline address book in a general special environment, such as displaying only some of the user's address information. To create a new offline address book, open the Exchange Managemen

Hyperopt Numpic 2012-paper-diginorm Ipython-notebooks Demo-weights Sarah Palin lda-Sarah Palin's email about topic modeling. Diffusion segmentation-a set of image segmentation algorithms based on the diffusion method. Scipy tutorials-scipy tutorial. It is out of date. Please refer to scipy-lecture-notes Crab-Python recommendation engine library. Bayesian inference tool in bayespy-Python. Scikit-learn tutorials-scikit-learn learning notes S

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

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

. The teacher is speaking in layman's terms, not worrying too much about math. And the work is also very suitable for beginners, are well-designed program framework, there is a job guide, according to the work guide to fill in the completed part of the line. This course is over, you can basically start a simple application of a variety of machine learning technology.2. Find a project or find a slightly more

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

In order to do the graduation design, learning Java, and then to connect the database, in order to connect the database to learn to do a simple small project, contacts (now only add features), successfully connected to the databaseFirst look at my Web home page:650) this.width=650; "src=" Https://s2.51cto.com/wyfs02/M00/96/0C/wKioL1kcQNmBzEr2AADJnAOoR8w473.png "title=" 1.png "Width=" "height=" 254 "border=" 0 "hspace=" 0 "vspace=" 0 "style=" width:500

Reprinted please indicate Source Address: http://www.cnblogs.com/xbinworld/archive/2013/04/21/3034300.html Pattern Recognition and machine learning (PRML) book learning, Chapter 1.1, introduces polynomial curve fitting) The doctor is almost finished. He will graduate next year and start preparing for graduation

Original writing. For more information, see http://blog.csdn.net/xbinworld,bincolumns. Pattern Recognition and machine learning (PRML) book learning, Chapter 1.1, introduces polynomial curve fitting) The doctor is almost finished. He will graduate next year and start preparing for graduation this year. He feels that he