[Machine learning algorithm-python implementation] matrix denoising and normalization, python Machine Learning1. The background project is required. We plan to use python to implement matrix denoising and normalization. The numpy mathematical library does not find ideal functions. Therefore, I wrote a de-noise and normalization algorithm in the standard library,
Core ML machine learning, coreml Machine Learning
At the WWDC 2017 Developer Conference, Apple announced a series of new machine learning APIs for developers, including visual APIs for facial recognition and natural language proce
Microsoft Azure cloud service introduces the machine learning module. Users only need to upload data and use some algorithm interfaces and R or other language interfaces provided by the machine learning module, you can use Microsoft Azure's powerful cloud computing capabilities to implement your
I. Working methods of machine learning
① Select data: Divide your data into three groups: training data, validating data, and testing data
② model data: Using training data to build models using related features
③ validation Model: Using your validation data to access your model
④ Test Model: Use your test data to check the performance of the validated model
⑤ Use model: Use fully trained models to mak
http://sourceforge.net/projects/numpy/files/download the corresponding version of the NumPy, everywhere, find a not python2.7Use Pip, please.Pip Install NumPyDownload finished, the hint does not install C + +, meaning is also to install VS2008, but installed is VS2012, had to download a VC for Pythonhttp://www.microsoft.com/en-us/download/confirmation.aspx?id=44266Re-pip, wait for the most of the day, the final count is successfulInput command introduced NumPyFrom numpy Import *Operation:InputRa
1. Common steps
2. Chinese participle
1 This is relative to the English text affective analysis, Chinese unique preprocessing.
2 Common methods: Based on the dictionary, rule-based, Statistical, based on the word annotation, based on artificial intelligence.
3 Common tools: Hit-language cloud, Northeastern University Niutrans statistical Machine translation system, the Chinese Academy of Sciences Zhang Huaping Dr. Ictclas, Posen technology, stutterin
The essential difference between classification and clustering in machine learning
There are two kinds of big problems in machine learning, one is classification, the other is clustering.In our life, we often do not have too much to distinguish between these two concepts, think clustering is classification, classificat
Octave Machine Learning Common commands
A, Basic operations and moving data around
1. Attach the next line of output with SHIFT + RETURN in command line mode
2. The length command returns a higher one-dimensional dimension when apply to the matrix
3. Help + command is a brief aid for displaying commands
4. doc + command is a detailed help document for displaying commands
5. Who command displays all current
This paper is organized from the "machine learning combat" and Http://write.blog.csdn.net/posteditBasic Principles of Mathematics:
Very simply, the Bayes formula:
Base of thought:
For an object to be sorted x, the probability that the thing belongs to each category Y1,y2, which is the most probability, think that the thing belongs to which category.Algorithm process:
1. Suppose something to be sorted x, it
1. Vector Norm
Norm, Norm, is a concept similar to "Length" in mathematics, which is actually a kind of function.The regularization (regularization) and sparse coding (Sparse coding) in machine learning are very interesting applications.For Vector a∈rn A\in r^n, its LP norm is | | a| | p= (∑IN|AI|P) 1p (1) | | a| | _p= (\sum_i^n |a_i|^p) ^{\frac 1 p} \tag 1Commonly used are:
L0 NormThe number of elements i
Cross entropy cost function 1. Cross-entropy theory
Cross entropy is relative to entropy, as covariance and variance.
Entropy examines the expectation of a single information (distribution):
H (p) =−∑I=1NP (xi) Logp (xi)
Cross-Entropy examines the expectations of two of information (distributions):H (P,Q) =−∑I=1NP (xi) logq (xi)For details, please see Wiki Cross entropy
y = Tf.placeholder (Dtype=tf.float32, Shape=[none, ten]) ...
Scores = Tf.matmul (H, W) + b
probs = Tf.nn.softmax (scores)
l
Recommended BooksHere is a list of books which I had read and feel it was worth recommending to friends who was interested in computer Scie nCE.Machine Learningpattern recognition and machine learningChristopher M. BishopA new treatment of classic machine learning topics, such as classification, regression, and time series analysis from a Ba Yesian perspective. I
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
Python Machine Learning Theory and Practice (5) Support Vector Machine and python Learning Theory
Support vector machine-SVM must be familiar with machine learning, Because SVM has alwa
Earlier, we mentioned supervised learning, which corresponds to non-supervised learning in machine learning. The problem with unsupervised learning is that in untagged data, you try to find a hidden structure. Because the examples provided to learners arenot marked, so there
As the name implies, the purpose of machine learning is to allow machines to have the ability to learn, understand, and comprehend things similar to human beings. Imagine how important it is for a patient's recovery if a computer can summarize and sum up a large number of cancer treatment records, and be able to give appropriate advice and advice to a physician. In addition to the medical field, financial s
increase or reduce the number of example (change 100 to 1000 or 10, etc.), reduce or increase the learning rate.elearning (Online learning)The previous algorithm has a fixed training set to train the model, when the model is well trained to classify and return the future example. Online learning is different, it updates the model parameters for each new example,
Original address: http://www.cnblogs.com/cyruszhu/p/5496913.htmlDo not use for commercial use without permission! For related requests, please contact the author: [Email protected]Reproduced please attach the original link, thank you.1 BasicsL Andrew NG's machine learning video.Connection: homepage, material.L 2.2008-year Andrew Ng CS229 machine LearningOf course
Public Course address:Https://class.coursera.org/ml-003/class/index
INSTRUCTOR:Andrew Ng 1. deciding what to try next (
Determine what to do next
)
I have already introduced some machine learning methods. It is obviously not enough to know the specific process of these methods. The key is to learn how to use them. The so-called best way to master knowledge is to put it into practice. Consider the ear
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