lasso machine learning

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Learning Log---Introduction to machine learning

Recommended book:Data mining: Practical machine learningData mining: Concepts and Techniques Han Jiawei; Read + reference articles later;Machine learning Combat (python);Machine learning Practical Case Analysis (r language);Neural networks and

Learning machine learning using Scikit-learn under Windows--Installation and configuration

Environment construction process is very troublesome ... But finally is ready, first give some of the process of reference to the more important information (find Microsoft's machine learning materials is a personal experience, without any reference):1. If the online various numpy, scipy and so on package installation tutorial trouble, go directly to: Microsoft Machine

A large-scale distributed depth learning _ machine learning algorithm based on Hadoop cluster

This article is reproduced from: http://www.csdn.net/article/2015-10-01/2825840 Absrtact: Deep learning based on Hadoop is an innovative method of deep learning. The deep learning based on Hadoop can not only achieve the effect of the dedicated cluster, but also has a unique advantage in enhancing the Hadoop cluster, distributed depth

Statistical learning Methods (2nd) Perceptual Machine Learning Notes

The 2nd Chapter Perception MachineThe Perceptron is a linear classification model of class Two classification, whose input is the characteristic vector of an instance, and the perceptual machine corresponds to the separation of the examples into positive and negative two classes in the input space (feature space), which belongs to the discriminant model. A loss function is introduced based on the error classification, and the loss function is minimize

Mathematical Learning in Machine Learning

To learn about machine learning, you must master a few mathematical knowledge. Otherwise, you will be confused (Allah was in this state before ). Among them, data distribution, maximum likelihood (and several methods for extreme values), deviation and variance trade-offs, as well as feature selection, model selection, and hybrid model are all particularly important. Here I will take you to review the releva

Andrew Ng's Machine Learning course Learning (WEEK4) Multi-Class classification and neural Networks

This semester has been to follow up on the Coursera Machina learning public class, the teacher Andrew Ng is one of the founders of Coursera, machine learning aspects of Daniel. This course is a choice for those who want to understand and master machine learning. This course

Machine learning Algorithm Basic Concept Learning Summary (reprint)

of a nonlinear function sigmoid, and the process of solving the parameters can be accomplished by the optimization algorithm. In the optimization algorithm, the gradient ascending algorithm is the most common one, and the gradient ascending algorithm can be simplified to the random gradient ascending algorithm.2.SVM (supported vector machines) Support vectors machine:Advantages : The generalization error rate is low, the calculation cost is small, the result is easy to explain.    cons : Sensit

Machine learning Combat Machines learning in Action code video project case

Machinelearning Everyone is welcome to participate and improve: a person can walk quickly, but a group of people can go farther Machine learning in Action (Robot learning Combat) | APACHECN (Apache Chinese web) Videos updated Weekly: If you feel valuable, please help dot Star "Follow-up organization learning

Common machine learning & data Mining Knowledge points "turn"

Turn from:"Basics" Common machine learning Data mining knowledge pointsBasis (Basic):MSE (Mean square error mean squared error), LMS (leastmean square min squared), LSM (Least square Methods least squares), MLE (Maximumlikelihood Estimation maximum likelihood estimation), QP (quadratic programming two-time plan), CP (Conditional probability conditional probability), JP (Joint probability joint probability)

July algorithm December machine learning online Class---20th lesson notes---deep learning--rnn

July algorithm December machine learning online Class---20th lesson notes---deep learning--rnnJuly algorithm (julyedu.com) December machine Learning Online class study note http://www.julyedu.com Cyclic neural networks Before reviewing the knowledge points:Full

Introduction to Machine learning

IntroductionIn real life, we may unknowingly use a variety of machine learning algorithms every day. For example, when you use Google every time, it works well, and one of the important reasons is that a learning algorithm implemented by Google can "learn" how to rank pages. Every time you use a Facebook or Apple photo-processing app, they can automatically ident

Machine learning------Bole Online

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

Evaluation and selection of "Machine learning 2nd Learning Notes" model

1. Training error: The error of the learner in the training set, also known as "experience Error"2. Generalization error: The error of the learner on the new sampleObviously, our goal is to get a better learner on a new sample, which is a small generalization error.3. Overfitting: The learner learns the training sample too well, leading to a decline in generalization performance (learning too much ...). Let me think of some people bookworm, reading de

Learning in the field of machine learning notes: Logistic regression & predicting mortality of hernia disease syndrome

say we have some data points, and now we use a straight line to fit these points, so that this line represents the distribution of data points as much as possible, and this fitting process is called regression.In machine learning tasks, the training of classifiers is the process of finding the best fit curve, so the optimization algorithm will be used next. Before implementing the algorithm, summarize some

Machine Learning note Bayesian Learning (top)

Machine learning Notes (i)Today formally began the study of machine learning, in order to motivate themselves to learn, but also to share ideas, decided to send their own experience of learning to the Internet to let everyone share.Bayesian learningLet's start with an exampl

Machine Learning Training Algorithm (optimization method) Summary--gradient descent method and its improved algorithm

Introduce Today will say two questions, first, suggest Bigfoot more look at Daniel's blog, Can rise posture ... For example: 1, focusing on language programming and application of the Liao Xuefeng https://www.liaoxuefeng.com/ 2, focus on the tall algorithm and open Source Library introduction of Mo annoying https://morvanzhou.github.io/ Second, deepen the understanding of machine learning algorithms. Person

Machine learning Algorithms Study Notes (3)--learning theory

Machine learning Algorithms Study NotesGochesong@ Cedar CedroMicrosoft MVPThis series is the learning note for Andrew Ng at Stanford's machine learning course CS 229.Machine learning Al

Machine learning techniques-deep learning

Course Address: Https://class.coursera.org/ntumltwo-002/lectureImportant! Important! Important!1. Shallow-layer neural networks and deep learning2. The significance of deep learning, reduce the burden of each layer of network, simplifying complex features. Very effective for complex raw feature learning tasks, such as machine vision, voice.In the following digita

Machine learning needs to read books _ Learning materials

If you only want to read a book, then recommend Bishop's Prml, full name pattern recognition and Machine Learning. This book is a machine learning Bible, especially for the Bayesian method, the introduction is very perfect. The book is also a textbook for postgraduate courses in ma

Machine Learning Algorithms (1)

scatterplot smoothing ). (2) instance-based algorithms Instance-based algorithms are often used to create models for decision-making problems. Such models often select a batch of sample data first, and then compare new data with the sample data based on some approximation. This method is used to find the best match. Therefore, instance-based algorithms are often referred to as "Winner-free" learning or "memory-based

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