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"Wunda Machine learning" Learning note--2.7 First learning algorithm = linear regression + gradient descent

gradient descent algorithm: linear regression Model:              Linear hypothesis:Squared difference cost function:By substituting each formula, the θ0 and θ1 are respectively biased:By substituting the partial derivative into the gradient descent algorithm, we can realize the process of finding the local optimal solution.The cost function of linear regression is always a convex function, so the gradient descent algorithm only has a minimum value after execution." Batch " gradient descent: use

Deep Learning Framework Google TensorFlow Learning notes one __ deep learning

TensorFlowTensorFlow is Google's second generation of AI learning systems based on Distbelief, whose name comes from its own operating principles. Tensor (tensor) means n-dimensional arrays, flow (stream) means the computation based on data flow diagram, TensorFlow flows from one end of the flow graph to the other. TensorFlow is a system that transmits complex data structures to artificial neural networks for analysis and processing. TensorFlow can be

Coursera Online Learning---section tenth. Large machine learning (Large scale machines learning)

First, how to learn a large-scale data set?In the case of a large training sample set, we can take a small sample to learn the model, such as m=1000, and then draw the corresponding learning curve. If the model is found to be of high deviation according to the learning curve, the model should continue to be adjusted on the existing sample, and the adjustment strategy should refer to the High deviation of se

Summary of machine learning Algorithms (12)--manifold learning (manifold learning)

1. What is manifoldManifold Learning Viewpoint: We think that the data we can observe is actually mapped by a low-dimensional pandemic to a high-dimensional space. Due to the limitations of the internal characteristics of the data, some of the data in the higher dimensions produce redundancy on the dimension, which in fact can be represented only by a lower dimension. So intuitively speaking, a manifold is like a D-dimensional space, in a m-dimensiona

Deep Learning Series (13) Transfer Learning and Caffe depth learning

1. Transfer Learning In practice, because of the size of the database, we usually do not start from scratch (random initialization of parameters) to train convolution neural networks. Instead, it is usually done on a large database (for example, Imagenet, a 1000-class image classification database with a total of 1.2 million) for CNN training, a trained network (hereinafter referred to as Convnet), and convnet in the following two ways to use our pro

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

Learning Programmer's technology is to improve their own outsourcing project learning exercise technology, or to the Internet company Learning Technology

, focused on collective solutions. The accumulation of knowledge and technology in those years is basically a very obvious feeling. Later for the sake of money I went into a company, but because of the opportunity to participate in the relatively high point of the product planning stage, found in this level can learn more advanced technology and business-related user theoretical basis, but also met a lot of real cattle, found that in fact a lot of Daniel, technology is not the main reason, Self-

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 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

Image classification based on depth learning classification with deep learning common model _ depth learning

probability, the probability that the return type is Softmax, and which highest result is evaluated. If you do a global system assessment, you can then add a layer of accuracy layer, the return type is accuracy. 3.2 2014 googlenet 2014 Imagenet Classification Detection Champion, 22-tier network ... To kneel, interested students to see the structure of the paper, where I can not cut off the screenshot ... In addition, give a few references: 1. Beginners to play: You can use the online convne

Learning notes for the Extreme Learning machine (Extreme learning machines)

Recent research on this one thing-the limit learning machine. In many problems, I often encounter two problems, one is classification, the other is regression. To put it simply, the classification is to label a bunch of numbers, and the regression is to turn a number into a number. Here we need to deal with the general dimension of the data is relatively high, in dealing with these two types of problems, the simplest way is weighted. The weight

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

"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

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