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The basic thought of deep learningSuppose we have a system s, which has n layers (S1,... SN), its input is I, the output is O, the image is expressed as: I =>S1=>S2=>.....=>SN = o, if the output o equals input I, that is, input I after this system changes without any information loss (hehe, Daniel said, it is impossible.) In the information theory, there is a "message-by-layer-loss" statement (processing inequalities), the processing of a information obtained B, and then the B processing to get

　　Statement: This blog post according to Http://www.ctocio.com/hotnews/15919.html collation, the original author Zhang Meng, respect for the original.Machine learning is undoubtedly a hot topic in the field of current data analysis. Many people use machine learning algorithms more or less in their usual work. This article summarizes common machine learning algori

Preface: "The foundation determines the height, not the height of the foundation!" The book mainly from the coding program, data structure, mathematical theory, data processing and visualization of several aspects of the theory of machine learning, and then extended to the probability theory, numerical analysis, matrix analysis and other knowledge to guide us into the world of machine learning! 1.1 Program

Python vector: Import NumPy as np a = Np.array ([[[1,2],[3,4],[5,6]]) SUM0 = Np.sum (A, axis=0) sum1 = Np.sum (A, Axis=1) PR int SUM0 Print sum1 > Results: [9 12][3 7] Dropout In the training process of the deep Learning Network, for the Neural network unit, it is temporarily discarded from the network according to certain probability.Dropout is a big kill for CNN to prevent the effect of fitting. Output is 10 categories, so the dimension is 10 Mod

First of all, I would like to introduce myself, I learned the public service management professional, can be said to be pure theory, community management of social management of the professional, but from the sophomore, the network and server operations to generate a strong interest, and continue to find relevant information on the Internet.During this time, the desktop environment from Ubuntu to Arch to the rumored elementary OS to CentOS and Debian, yes, and now the response is good Deepin Lin

Deep Learning (depth learning) Learning notes finishing (ii) Transferred from: http://blog.csdn.net/zouxy09 Because we want to learn the characteristics of the expression, then about the characteristics, or about this level of characteristics, we need to understand more in-depth point. So before we say deep learning,

Stanford University's Machine learning course (The instructor is Andrew Ng) is the "Bible" for learning computer learning, and the following is a lecture note.First, what is machine learningMachine learning are field of study that gives computers the ability to learn without being explicitly programmed.In other words,

Chapter I. Introduction to Statistical learning methodsThe main features of statistical learning are: (1) Statistical learning is based on computers and networks, and is based on computer and network ; (2) Statistical learning takes data as the research object and is a data-driven discipline; (3) Un

C Language Learning second-c language basic learning, language learning second-c 1. Standard C Language C language was born in 1970s. It was older than ourselves. Many standards were generated during this period, but various compilers have different support for the standards.Ansi c is the most widely used standard and the first formal standard, known as "Standard

Linux O M learning notes-MySQL Log learning and learning notes-mysql I. Error Log: Error Log 1. Introduction An error log is an error message that records the MySQL service process mysqld during startup, shutdown, or running. The error log function is enabled by default. In addition, error logs cannot be disabled. By default, error logs are stored in mysql datab

Ansible learning-simple learning notes 2, ansible-learning notes Roles is used for hierarchical and structured organization playbook, and the encryption process in previous note 1 is used. My directory svnrepos has two directories. Ansible_test and test Under the test directory: The directory structure of ansible_test is: The file content is as foll

JQuery Learning Content summary. Learning manual, jquery learning Manual JQuery query manual: I. JQuery usage 1. First download the Jquery js file and load the js file using the Enter the JQuery code in the next line: 2. JQuery code starts with the following code: Complete Syntax: $ (document). ready (function () {JQuery code }) Simple Syntax: $ (function () {

System-based learning, system-based learning, and system-based Learning Generally, you can avoid code writing based on the following eight principles:90%-100% adventure competition caused by the OpenGL code:1) time series logic ---- use non-blocking assignment2) latches ---- use non-blocking assignment3) combination logic generated using the always block-assign

Custom View learning notes: Path-based learning notes: learning notes I. besell curve SourceIn the field of Numerical Analysis of mathematics, the besell curve is a very important parameter curve in computer graphics. A higher dimension is called the besell curve. The besell triangle is a special example.These two articles are the most clear explanations I have

Python learning notes day5-common module learning and python learning notes day5 I. Main Content Ii. Details 1. Module A. Definition: the essence is the python file ending with. py. It logically organizes python code to implement certain functions. For example, the file name is test. py --> Module name test. B. Import method: imort moduname From mdname import * F

Transformation:jquery Object [0] = + DOM Object$ (Dom object) = = jquery Object1.id$ ("#id")2.class$ (". CN")3. Get all the A tags in the label$ (' a ')4. Get a collection of multiple labels$ ("a,.cn. #in")5. Hierarchy$ ("#in a") all a labels with an ID of n$ ("#in >a") son level6. Index$ ("#i10 A:eq (2)") ID i10 a label with index value 2And there is: first/: Last7. Filter Properties$ (' [Alex] ')$ ("[Alex=value]")Shorthand: $ (": Value")8. Set disabled in the label can be set to non-editable$

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

1. Write in frontSupervised learning (supervised learning), unsupervised learning (unsupervised learning), and semi-supervised learning (semi-supervised learning) in the field of machine learn