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Learning the learning notes series of OpenCV-Environment configuration 2, opencv learning notes

Learning the learning notes series of OpenCV-Environment configuration 2, opencv learning notes To learn OpenCV well, you must first know how to configure the environment. Take your own configuration environment as an example. The steps are as follows. Step 1 download and decompress the OpenCV source code Although many third-party websites and some

Excellent open source Software Learning Series (i)--from zero learning Spring4 and learning method sharing

: How do I check out a branch from GitHub?Plan 5:git Tools How to use————————————————————————Attention:1. Every time you meet a new plan, you should not immediately go into the planning of learning, because these problems are often very complex to learn, and its learning as much as the spring Web site, such as learning git tools, you can not spring has not been t

Stanford Machine Learning video note WEEK6 on machine learning recommendations Advice for applying machines learning

We will learn how to systematically improve machine learning algorithms, tell you when the algorithm is not doing well, and describe how to ' debug ' your learning algorithms and improve their performance "best practices". To optimize machine learning algorithms, you need to understand where you can make the biggest improvements. We will discuss how to understand

Machine Learning School Recruit Note 3: Integrated Learning adaboost_ Machine learning

The method of Ascension is to start from the weak learning algorithm, to learn, to get a series of weak classifier (basic classifier), and then combine these weak classifiers, build a strong classifier. Most of the lifting methods change the probability distribution (weight distribution) of training data, call the weak learning algorithm according to different training data distribution, and learn a series

Deep Learning (bot direction) learning notes (1) Sequence2sequence Learning

Series Catalog:Seq2seq chatbot chat Robot: A demo build based on Torch CodexDeep Learning (bot direction) learning notes (1) Sequence2sequence LearningDeep Learning (bot direction) learning Notes (2) RNN Encoder-decoder and LSTM study 1 preface This deep learning, in fact, i

Deep Learning 11 _ Depth Learning UFLDL Tutorial: Data preprocessing (Stanford Deep Learning Tutorial)

theoretical knowledge : UFLDL data preprocessing and http://www.cnblogs.com/tornadomeet/archive/2013/04/20/3033149.htmlData preprocessing is a very important step in deep learning! If the acquisition of raw data is the most important step in deep learning, then the preprocessing of the raw data is an important part of it.1. Methods of data preprocessing :① Data Normalization :Simple Scaling : Re-adjusts the

[Web Development Learning Notes] Hibernate learning summary, learning notes hibernate

[Web Development Learning Notes] Hibernate learning summary, learning notes hibernateHibernate learning notes part: This part of learning is easier, the code is more comprehensive, and easy to understand. It can be said that it is something of a memory nature. I did not take

Learning notes for "Machine Learning Practice": two application scenarios of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k-

Learning notes for "Machine Learning Practice": two application scenarios of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k- After learning the implementation of the k-Nearest Neighbor Algorithm, I tested the k-Nearest Neighbor Algorithm by referring to the examples in machine

Deep Learning Framework Paddlepdddle Learning (i) _ depth learning

Paddlepaddle is Baidu Open source of a deep learning framework, according to its official website of the document used to learn.This article describes its installation.-Operating systemThe official website document uses the operating system is ubunt14.04, I use is the VMware Workstation player installs the Ubuntu virtual machine, it and redhat some different, but the configuration is troublesome, the DNS configuration and the resolution reference some

My Python self-learning Path 1: Python learning path and python self-learning path

My Python self-learning Path 1: Python learning path and python self-learning path As a hacker, when learning Python, he will inevitably take some detours. Some people may lose themselves in the detours and others may get out of the detours. I am not a member of the company, so I want to talk about how to learn Python

Ios learning notes --- ios learning route, ios learning notes --- ios

Ios learning notes --- ios learning route, ios learning notes --- ios Complete ios learning route Images downloaded from the internet I am not a big bull. I write a blog to record my learning process. This is not an entry-level lea

Machine LEARNING-XVII. Large Scale machines Learning large machine learning (Week 10)

http://blog.csdn.net/pipisorry/article/details/44904649Machine learning machines Learning-andrew NG Courses Study notesLarge Scale machines Learning large machine learningLearning with Large datasets Big Data Set LearningStochastic Gradient descent random gradient descentMini-batch Gradient descent mini batch processing gradient descentConvergence of random gradi

Andrew Ng's Machine Learning course learning (WEEK5) Neural Network Learning

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 covers some of the basic concepts and methods of machine

Stanford University public Class machine learning: Advice for applying machines learning-deciding to try next (how to determine the most appropriate and correct method when designing a machine learning system)

If we are developing a machine learning system and want to try to improve the performance of a machine learning system, how do we decide which path we should choose Next?In order to explain this problem, to predict the price of learning examples. If we've got the learning parameters and we're going to test our hypothet

Deep Learning Challenge: Extreme Learning Machine (extra-limited learning machine)?

Preface: Today just heard a talk about Extreme learning Machine (Super limited learning machine), the speaker is Elm Huangguang Professor . The effect of elm is naturally much better than the SVM,BP algorithm. and relatively than the current most fire deep learning, it has a great advantage: the operation speed is very fast, accurate rate is high, can online se

"Original" Learning Spark (Python version) learning notes (iv)----spark sreaming and Mllib machine learning

  Originally this article is prepared for 5.15 more, but the last week has been busy visa and work, no time to postpone, now finally have time to write learning Spark last part of the content.第10-11 is mainly about spark streaming and Mllib. We know that Spark is doing a good job of working with data offline, so how does it behave on real-time data? In actual production, we often need to deal with the received data, such as real-time machine

Learning OpenCV learning notes series (3) display pictures and videos, opencv learning notes

Learning OpenCV learning notes series (3) display pictures and videos, opencv learning notes OpenCV is a computer vision library, so there are only two objects to process: "Images" and "videos" (in fact, videos are also extracted into single-frame images for processing. In general, or image processing ). To learn OpenCV, you must first know how OpenCV opens the "

Stanford University public Class machine learning: Advice for applying machines learning-evaluatin a phpothesis (how to evaluate the assumptions given by the learning algorithm and how to prevent overfitting or lack of fit)

How to evaluate the assumptions we get from our learning algorithms and how to prevent overfitting and less-fitting problems.When we determine the parameters of the learning algorithm, we consider the choice of parameters to minimize the training error. Some people think that getting a small training error must be a good thing. But in fact, just because this hypothesis has a very small training error, when

Deep learning Deep Learning with MATLAB (Lazy person Version) _ Depth Learning

In the words of Russian MYC although is engaged in computer vision, but in school never contact neural network, let alone deep learning. When he was looking for a job, Deep learning was just beginning to get into people's eyes. But now if you are lucky enough to be interviewed by Myc, he will ask you this question Deep Learning why call Deep

[Pattern Recognition and machine learning] -- Part2 Machine Learning -- statistical learning basics -- regularized Linear Regression

Source: https://www.cnblogs.com/jianxinzhou/p/4083921.html1. The problem of overfitting (1) Let's look at the example of predicting house price. We will first perform linear regression on the data, that is, the first graph on the left. If we do this, we can obtain such a straight line that fits the data, but in fact this is not a good model. Let's look at the data. Obviously, as the area of the house increases, the changes in the housing price tend to be stable, or the more you move to the right

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