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
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
Machine Learning Summary (1), machine learning SummaryIntelligence:The word "intelligence" can be defined in many ways. Here we define it as being able to make the right decision based on certain situations. Knowledge is required to make a good decision, and this knowledge must be operable, for example, interpreting se
If we are developing a machine learning system and want to try to improve the performance of a machine learning system, how do we decide which path we should choose Next?In order to explain this problem, to predict the price of learning examples. If we've got the
Preface: Today just heard a talk about Extreme learning Machine (Super limited learning machine), the speaker is Elm Huangguang Professor . The effect of elm is naturally much better than the SVM,BP algorithm. and relatively than the current most fire deep learning, it has
Https://github.com/josephmisiti/awesome-machine-learning#julia-nlp
Julia
General-purpose Machine Learning
Machinelearning-julia Machine Learning LibraryMlbase-a set of functions to support development of
This article focuses on the contents of the 1.2Python libraries and functions in the first chapter of the Python machine learning time Guide. Learn the workflow of machine Learning.I. Acquisition and inspection of dataRequests getting dataPandans processing Data1 ImportOS2 ImportPandas as PD3 ImportRequests4 5PATH = R'E:/python
in the process of learning rate can be seen as the length of the descent process, assuming that your step is very big can cross the valley directly on the opposite side of the mountain, it is difficult to get the local optimal solution. At this point, reducing the step size will increase your chances of going to the ground.2. About the cross fittingBy using the methods of drop out, batch normalization and data argument, the generalization ability of
Recently saw a relatively good machine learning course, roughly heard it again. The overall sense of machine learning field is still more difficult, although Li Hongyi teacher said is very good, not enough to absorb up or have a certain difficulty. Even though the process has been told, it is difficult to understand ho
Source: https://www.cnblogs.com/jianxinzhou/p/4083921.html1. The problem of overfitting
(1)
Let's look at the example of predicting house price. We will first perform linear regression on the data, that is, the first graph on the left. If we do this, we can obtain such a straight line that fits the data, but in fact this is not a good model. Let's look at the data. Obviously, as the area of the house increases, the changes in the housing price tend to be stable, or the more you move to the right
What are machine learning?The definitions of machine learning is offered. Arthur Samuel described it as: "The field of study that gives computers the ability to learn without being explicitly prog Rammed. " This was an older, informal definition.Tom Mitchell provides a more modern definition: 'a computer program was sa
Bayesian Introduction Bayesian learning Method characteristic Bayes rule maximum hypothesis example basic probability formula table
Machine learning learning speed is not fast enough, but hope to learn more down-to-earth. After all, although it is it but more biased in mathematics, so to learn the rigorous and thoroug
Python Machine Learning Theory and Practice (6) Support Vector Machine and python Learning Theory
In the previous section, the theory of SVM is basically pushed down, and the goal of finding the maximum interval is finally converted to the problem of solving the alpha of the Child variable of the Laplace multiplication
Knowing an algorithm and using an algorithm are two different things.
What should I do if I find that the model has a big error after you train the data?
1) Obtain more data. It may be useful.
2) reduce feature dimensions. You can manually select one or use mathematical methods such as PCA.
3) Obtain more features. Of course, this method is time-consuming and not necessarily useful.
4) add polynomial features. Are you trying to save your life?
5) Build your own, new, and better features. A litt
Machine Learning 4, machine learning
Probability-based classification method: Naive BayesBayesian decision theory
Naive Bayes is a part of Bayesian decision-making theory. Therefore, before explaining Naive Bayes, let's take a quick look at Bayesian decision-making theory knowledge.
The core idea of Bayesian decision-m
In machine learning, often need to calculate the distance between each sample, used for classification, according to distance, different samples grouped into a class; But in the current machine learning algorithm, the distance calculation mode is endless, then this blog is mainly to comb the current
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