h20 machine learning

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Recommended! Machine Learning Resources compiled by programmers abroad)

This article is translated from awesome-machine-learning by bole online-toolate. Welcome to the technical translation team. For more information, see the requirements at the end of the article. This article has compiled some frameworks, libraries, and software (sorted by programming language) in the machine learning fi

Machine Learning Resources overview [go]

This article has compiled some frameworks, libraries, and software (sorted by programming language) in the machine learning field ).C ++ Computer Vision CCV-Machine Vision Library Based on C Language/provided Cache/core, novel machine vision Library Opencv-it provides C ++, C, Python, Java and Matlab interfaces, and

Teaching machines to understand us let the machine understand our belief in three natural language learning and deep learning

software that defeats a number of human participants in an IQ test that requires understanding synonyms, antonyms, and analogies.LeCun ' s group is working on going further. "Language in itself are not so complicated," he says. "What's complicated is have a deep understanding of language and the world that gives you common sense. That's what we ' re really interested in building into machines. " LeCun means common sense as Aristotle used the term:the ability to understand basic physical reality

Python machine learning: 6.3 Debugging algorithms using learning curves and validation curves

under-fitting with verification curveValidating a curve is a very useful tool that can be used to improve the performance of a model because he can handle fit and under-fit problems.The verification curve and the learning curve are very similar, but the difference is that the accuracy rate of the model under different parameters is not the same as the accuracy of the different training set size:We get the validation curve for parameter C.Like the Lea

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

Machine-learning Course Learning Summary (1-4)

First, Introduction1. Concept : The field of study that gives computers the ability to learn without being explicitly programmed. --an older, informal definition by Arthur Samuel (for tasks that cannot be programmed directly to enable the machine to learn) "A computer program was said to learn from experience E with respect to some class of tasks T and performance measure P, if Its performance on tasks in T, as measured by P, improves wit

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

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

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

Definition of machine learning (learning)

There are two definitions related to machine learning:1) give the computer the research field of learning ability without fixed programming.2) A computer program that can learn from a number of tasks (T) and performance metrics (P), Experience (E). In learning, the performance p of task t can improve experience E with

Stanford University public Class machine learning: Machines Learning System Design | Error metrics for skewed classes (definition of skew class issues and evaluation measures for skew class issues: precision ratio (precision) and recall rate (recall))

0.5% of the patients in our screening program are suffering from cancer. In this case, the error rate of 1% is no longer as good.For example, here is a line of code that ignores the input value x, so that y is always equal to 0, so it always predicts that no one has cancer. Then this algorithm actually has only 0.5% error rate. So this is even better than the 1% error rate we got before, which is a non-machine le

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

R Language Learning notes-machine learning 1-3 Chapters

After tossing the crawler and some interesting content, I recently in the R language for simple machine learning knowledge, the main reference is "machine learning-Practical Case Analysis" this book.This book is a rare, purely r language-based machine

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

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

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