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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

　　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 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 "

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

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

deep understanding of machine learning: Learning Notes from principles to algorithms-1th week 02 easy to get started Deep understanding of machine learning from principle to algorithmic learning notes-1th week 02 Easy to get started 1 General model statistical learning theo

Machine learning Types Machine Learning Model Evaluation steps Deep Learning data Preparation Feature Engineering Over fitting General process for solving machine learning problems Machine Learning Four BranchesThe second classification, multi-classi

The motive and application of machine learningTools: Need genuine: Matlab, free: Octavedefinition (Arthur Samuel 1959):The research field that gives the computer learning ability without directly programming the problem.Example: Arthur's chess procedure, calculates the probability of winning each step, and eventually defeats the program author himself. (Feel the idea of using decision trees)definition 2(Tom Mitchell 1998):A reasonable

CSS learning notes -- learning to locate the position attribute and learning notes position One of the remaining questions before learning today is the position attribute of CSS. First, problems related to position are summarized: The first question: Which of the following attributes does position have? For the positio

Q-learning Source code Analysis.Import Java.util.random;public class qlearning1{private static final int q_size = 6; Private static final Double GAMMA = 0.8; private static final int iterations = 10; private static final int initial_states[] = new int[] {1, 3, 5, 2, 4, 0}; private static final int r[][] = new int[][] {{-1,-1,-1,-1, 0,-1}, { -1,-1,-1, 0,-1, 100}, {-1,-1,-1, 0,-1,-1}, {-1, 0, 0,

Objective:When looking for a job (IT industry), in addition to the common software development, machine learning positions can also be regarded as a choice, many computer graduate students will contact this, if your research direction is machine learning/data mining and so on, and it is very interested in, you can consider the post, After all, machine learning ca

Students in the field of machine learning know that there is a universal theorem in machine learning: There is no free lunch (no lunch). The simple and understandable explanation for it is this: 1, an algorithm (algorithm a) on a specific data set than the performance of another algorithm (algorithm B) at the same time, it must be accompanied by algorithm A on the other specific data set of the performanc

Reading List List of reading lists and survey papers:BooksDeep learning, Yoshua Bengio, Ian Goodfellow, Aaron Courville, MIT Press, in preparation.Review PapersRepresentation learning:a Review and New perspectives, Yoshua Bengio, Aaron Courville, Pascal Vincent, ARXIV, 2012. The monograph or review paper Learning deep architectures for AI (Foundations Trends in Machine

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