ruby machine 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

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

The difference between Ruby learning notes-puts,p,printCommon denominator: Both are used for screen output.Different points:Puts the output will be wrapped (if the content parameter is empty, only one newline symbol is output), and if there is an escape character in the content parameter, the output will first process the escape and then outputP is basically the same as puts, but does not handle escape symb

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,

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

Open source, cross-platform (Windows, Linux, UNIX, etc.)scripting language (less efficient), no need to compile, run directlyGrammar Nature, IntroductionEverything is an object.Flexible language design (feel free to change the original design)Powerful nlocks and Cloures.Inheritance and MixinRuby Gems (plugins, libraries, etc.)Ruby Interpreter (interpreter): Mri,jruby,rubiniusInstallation under CentOSYum Install-y RubyVersion

One, Redis is a support transaction, please see this introduction.Here are a few of the commands that Ruby implements, Multi,exec,discard,watch,unwatch.1,multi: Mark a transaction block start, exec: Execute all multi after the command issuedRequire'Redis'R=Redis.newr.set'a', 1R.set'b', 2R.multir.set'a', 3R.set'b', 6R.exec#r.get ' a '#= "3"#r.get ' B '#= "6"2,discard: Discard all MULTI post-order commandsRequire'Redis'R=Redis.newr.set'a', 1R.set'b', 2R

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

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

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

1 # global variable 2 $ global_variable = 103 class class14 def print_global () 5 puts "global variable in class1 is # {$ global_variable}" 6 end7 end8 class1obj = class1.new9 class1obj. print_global () 1 # instance variable 2 Class Customer 3 def initialize (ID, name, ADDR) 4 @ cust_id = ID 5 @ cust_name = Name 6 @ cust_addr = ADDR 7 end 8 def display_detalis 9 puts "Customer ID #{@ cust_id}" 10 puts "customer name #{@ cust_name} "11 puts" customer ADDR # {@ cust_addr} "12 end13 end14 cust1 = c

1. Relationship of classes and instancesUse "instance. Class" To see which class an object belongs toUse "instance. Instance_of (class name)" To determine whether the instance belongs to a classUse "instance. Instance_methods" To view all instance methods of a classThe 2.BasicObject class is the parent class of all classes, it only defines the most basic method of the Ruby object, even the general object needs not the function, GU general inherits the

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

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

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

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

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