teched learning

In general, the relationship between recall and precision is as follows:1, if the need for a high degree of confidence, the precision will be very high, the corresponding recall rate is very low, 2, if the need to avoid false negative, the recall rate will be very high, the precision will be very low. on the right, the relationship between recall rate and precision ratio is shown in a learning algorithm. It is important to note that no

Learning notes for "Machine Learning Practice": Implementation of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k- The main learning and research tasks of the last semester were pattern recognition, signal theory, and image processing. In fact, these fields have more or less intersection with machine

Learning notes TF053: Recurrent Neural Network, TensorFlow Model Zoo, reinforcement learning, deep forest, deep learning art, tf053tensorflow Recurrent Neural Networks. Bytes. Natural language processing (NLP) applies the network model. Unlike feed-forward neural network (FNN), cyclic networks introduce qualitative loops, and the signal transmission does not disa

non-supervised learning:watermark/2/text/ahr0cdovl2jsb2cuy3nkbi5uzxqvdtaxmzq3njq2na==/font/5a6l5l2t/fontsize/400/fill/i0jbqkfcma==/ Dissolve/70/gravity/southeast ">In this way of learning. The input data part is identified, some are not identified, such a learning model can be used to predict, but the model first need to learn the internal structure of the data in order to reasonably organize the data to be

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clustering, dimensionality reduction, anomaly detection, large-scale machine learning and other

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

on a 64-bit system,% ProgramFiles (x86) %\ Microsoft Silverlight \ 2.0.31005.0 \ sos. dll). Just attach your debugger to the unmanaged browser to debug the managed Silverlight application. At this point you might should CT me to announce that I'm going to do some long series on how to adjust tively use SOS, but that's not the case for two reasons: first, i'm not going to dump out a series of screen shots when your others have done this already. second, 90% + of advanced debugging is all about u

Continue to learn http://www.cnblogs.com/tornadomeet/archive/2013/03/15/2962116.html, the last class learning rate is fixed, and here we aim to find a better learning rate. We mainly observe the different learning rate corresponding to the different loss value and the number of iterations between the function curve is how to find the fastest convergence of the fu

Non-supervised learning: In this learning mode, the input data part is identified, the part is not identified, the learning model can be used for prediction, but the model first needs to learn the internal structure of the data in order to reasonably organize the data to make predictions. The application scenarios include classification and regression, and t

Original: Image classification in 5 Methodshttps://medium.com/towards-data-science/image-classification-in-5-methods-83742aeb3645 Image classification, as the name suggests, is an input image, output to the image content classification of the problem. It is the core of computer vision, which is widely used in practice. The traditional method of image classification is feature description and detection, such traditional methods may be effective for some simple image classification, but the tradit

Transferred from: http://blog.csdn.net/zouxy09/article/details/8775488 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, we need to re-talk about the characteristics (hehe, actually see so good interpretation of the characteristics, not put here a little pity, so it was stuffed here). Iv. Abo

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$