Bengio, LeCun, Jordan, Hinton, Schmidhuber, Ng, de Freitas and OpenAI had done Reddit AMA's. These is nice places-to-start to get a zeitgeist of the field.Hinton and Ng lectures at Coursera, UFLDL, cs224d and cs231n at Stanford, the deep learning course at udacity, and the sum Mer School at IPAM has excellent tutorials, video lectures and programming exercises that should help you get STARTED.NB Sp The online book by Nielsen, notes for cs231n, and blo
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
Original address:http://blog.csdn.net/miscclp/article/details/6339456Under the traditional machine learning framework, the task of learning is to learn a classification model based on a given sufficient training data, and then use this learning model to classify and predict the test document. However, we see that the machine
Programming Libraries Programming Library ResourcesI am an advocate of the concept of "learning to be adventurous and try." This is the way I learn programming, I believe many people also learn to program design. First understand your ability limits, then expand your ability. If you know how to program, you can draw on the experience of programming quickly to learn more about machine learning. Before you im
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
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
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
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
In fact, there are many ways to learn about machine learning and many resources such as books and open classes. Some related competitions and tools are also a good helper for you to understand this field. This article will focus on this topic, give some summative understanding, and provide some learning guidance for the transformation from programmers to machine learnin
Under the traditional machine learning framework, the task of learning is to learn a classification model based on a given sufficient training data, and then use this learning model to classify and predict the test document. However, we see that the machine learning algorithm has a key problem in the current research o
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
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
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