WEEK1:Machine learning:
A computer program was said to learn from experience E with respect to some class of tasks T and performance measure P, if Its performance on tasks in T, as measured by P, improves with experience E.
Supervised learning:we already know what we correct output should look like.
Regression:try to map input variables to some continuous function.
Simple examples are used to understand what machine learning is, and examples are used to understand machine learning.
1. What is machine learning?
What is machine
Original: http://blog.csdn.net/abcjennifer/article/details/7834256This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Support vector machines), clustering, dimensionality reduc
Original handout of Stanford Machine Learning Course
This resource is the original handout of the Stanford machine learning course, which is AndrewNg said that a total of 20 PDF files cover some important models, algorithms, and concepts in machine
"Machine learning" Matlab 2015a self-machine learning algorithm RollupAuthor: Chen Fa St.
"Introduction"Today suddenly found that the version of matlab2015a with a lot of classical machine learning methods, simple and easy to use,
1.1 machine learning basics-python deep machine learning, 1.1-python
Refer to instructor Peng Liang's video tutorial: reprinted, please indicate the source and original instructor Peng Liang
Video tutorial: http://pan.baidu.com/s/1kVNe5EJ
1. course Introduction
2. Machine
ProfileThis article is the first of a small experiment in machine learning using the Python programming language. The main contents are as follows:
Read data and clean data
Explore the characteristics of the input data
Analyze how data is presented for learning algorithms
Choosing the right model and
some time ago on the Internet to see the Coursera Open Classroom Big Machine learning Cornerstone Course, more comprehensive and clear machine learning needs of the basic knowledge, theoretical basis to explain. There are several more important concepts and ideas in foundation, first review, and then open the follow-up
to the right in this image. We can generally see the two learning curves, the two curves of blue and red are approaching each other. Therefore, if we extend the curve to the right, it seems that the training set error is likely to increase gradually. The cross-validation set error will continue to decline. Of course, we are most concerned with cross-validation set errors or test set errors. So from this picture, we can basically predict that if we co
This is a creation in
Article, where the information may have evolved or changed.
Follow the code to learn Golang language. Today explains the basic types of Golang languages, and describes Boolean types and numeric types.
Learning Golang Language (1): Hello World
Learning
Follow the code to learn Golang language. Today explains the basic types of Golang languages, and describes Boolean types and numeric types.
Learning Golang Language (1): Hello World
Learning Golang Language (2): Variables
Application Recommendations for machine learningFor a long time, the machine learning notes have not been updated, the last part of the updated neural network. This time we'll talk about the application of machine learning recommendations.Decide what to do nextSuppose we nee
Original address: http://blog.csdn.net/lrs1353281004/article/details/79529818
Sorting out the machine learning-algorithm engineers need to master the basic knowledge of machine learning, and attached to the internet I think that write a better blog address for reference. (Continuous update)
1. What is machine learningMachine learning is the conversion of unordered data into useful information.The main task of machine learning is to classify and another task is to return.Supervised learning: It is called supervised learning
Public Course address:Https://class.coursera.org/ml-003/class/index
INSTRUCTOR:Andrew Ng 1. Learning with large datasets (
Big Data Learning
)
The importance of data volume has been mentioned in the previous lecture on machine learning design. Remember this sentence:
It is not who has the best algorithm that w
I. BACKGROUND
In machine learning, there are 2 great ideas for supervised learning (supervised learning) and unsupervised learning (unsupervised learning)
Supervised learning, in layman
July online April machine learning algorithm class notes--no.1
Objective
Machine learning is a multidisciplinary interdisciplinary, including probability theory, statistics, convex analysis, feature engineering and so on. Recently followed the July algorithm to learn the knowledge of
This is a creation in
Article, where the information may have evolved or changed.
Learning Golang Language (1): Hello World
Learning Golang Language (2): Variables
Learning Golang Language (3): Type--Boolean and numeric types
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