Machine learning practices in python3.x and python machine learning practices
Machine Learning Practice this book is written in the python2.x environment, while many functions and 2 in python3.x. the names or usage methods in x ar
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
1. Decision Tree applicable conditions: The data of different class boundary is non-linear, and by continuously dividing the feature space into a matrix to simulate. There is a certain correlation between features. The number of feature values should be similar, because the information gain is biased towards more numerical characteristics. Advantages: 1. Intuitive decision-making rules; 2. Nonlinear characteristics can be handled; 3. The interaction between variables is considered. Disadvanta
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
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,
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
For a given set of data and problems, the machine learning method to solve the problem is generally divided into 4 steps:
A Data preprocessing
First, you must ensure that the data is in a format that meets your requirements. The standard data format can be used to fuse algorithms and data sources to facilitate matching operations. In addition, you need to prepare specific data formats for
First, the machine learning algorithm engineers need to master the skills
Machine Learning algorithm engineers need to master skills including
(1) Basic data structure and algorithm tree and correlation algorithm graph and correlation algorithm hash table and correlation algorithm matrix and correlation algorithm
intervention on the results of model training it's a lever. Model does not understand the business, really understand the business is people. What the model can do is to learn from the cost function and sample, and find the optimal fit of the current sample. Therefore, machine learning workers should be appropriate to the needs of the characteristics of some human intervention and "guidance", such as the h
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
p.s. SVM is more complex, the code is not studied clearly, further learning other knowledge after the supplement. The following is only the core of the knowledge, from the "machine learning Combat" learning summary. Advantages:The generalization error rate is low, the calculation cost is small, the result is easy to ex
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
The concept of extreme learning machineElm is a new fast learning algorithm, for TOW layer neural network, elm can randomly initialize input weights and biases and get corresponding output weights.For a single-hidden-layer neural network, suppose there are n arbitrary samples, where。 For a single hidden layer neural network with a hidden layer node, it can be expressed asWhere, for the activation function,
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
In the introduction of recommendation system, we give the general framework of recommendation system. Obviously, the recommendation method is the most core and key part of the whole recommendation system, which determines the performance of the recommended system to a large extent. At present, the main recommended methods include: Based on content recommendation, collaborative filtering recommendation, recommendation based on association rules, based on utility recommendation, based on knowledge
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
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