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nodes on the node on behalf of a variety of fractions, example to get the classification result of Class 1The same input is transferred to different nodes and the results are different because the respective nodes have different weights and biasThis is forward propagation.10. MarkovVideoMarkov Chains is made up of state and transitionsChestnuts, according to the phrase ' The quick brown fox jumps over the lazy dog ', to get Markov chainStep, set each word to a state, and then calculate the prob

parallel. However, partial parallelism can be achieved by self-sampling SGBT.8, GBDTAdvantages: 1, can flexibly deal with various types of data, including continuous and discrete values, processing classification and regression problems, 2, in the relatively few parameters of the time, the forecast preparation rate can also be relatively high. This is relative to the SVM, 3, can be used to filter features.4, using some robust loss function, the robustness of outliers is very strong. such as Hub

data arrives. The training set requirements for supervised learning include input and output, which can also be characterized and targeted. The goal of the training set is to be labeled (scalar) by the person. Under supervised learning, the input data is called "training data", each set of training data has a clear identification or results, such as anti-spam system "spam", "non-spam", the handwritten nume

input and output, which can also be characterized and targeted. The goal of the training set is to be labeled (scalar) by the person. Under supervised learning, the input data is called "training data", each set of training data has a clear identification or results, such as anti-spam system "spam", "non-spam", the handwritten numeral recognition of "1", "2", "3" and so on. In the establishment of the predictive model, supervised

Summaryhave been interested in machine learning, has no time to study, today is just the weekend, have time to see the major technical forum, just see a good machine learning article, here to share to everyone.Machine learning is undoubtedly a hot topic in the field of curre

of artificial neural network using inexpensive and redundant computational resources. This type of approach attempts to resume a much larger and more complex neural network, as mentioned earlier, many methods are based on very limited tag data in large data sets to solve semi-supervised learning problems.Limited Boltzmann Machine RBMDepth of belief net dbmconvolutional Neural NetworksCascade Automatic Enco

space corresponds to a feature. Sometimes it is assumed that the input space and the feature space are the same space, they are not differentiated, sometimes it is assumed that the input space and the feature space are different spaces, the instance is mapped from the input space to the feature space. The model is actually defined on the feature space. This provides a good basis for the classification of machine

This paper mainly includes the realization of common machine learning algorithms, in which the mathematical derivation, principle and parallel implementation will give the link.
Machine Learning (machines

; Rsold =r " *R; for i=1:length (b) Ap =a*P; Alpha =rsold/(p " *ap); X=x+alpha*P; R =r-alpha*AP; Rsnew =r " *R; if sqrt (rsnew) break ; End P =r+ (rsnew/rsold) *P; Rsold =rsnew; EndEnd Back to top of 4. Heuristic Optimization methodHeuristic method refers to the method that people take when they solve the problem and find it according to the rule of experience. It is characterized by the use of past experience in the solution of problems, th

exception, that is, the meaning of expectation. E-step is also the process of acquiring expectations. Based on the existing model, the results of each observation data input into the model are calculated. This process is called the expected value calculation process, the E process.The full name of M-step:m is maximization, which means maximizing. M-step is also the process of maximizing expectations. After you get a round of expectations, recalculate the model parameters to maximize the expecte

data to determine the classification, so there is a certain error rate in the classification decision.Application:(1) Sentiment analysis(2) Document classification(3) Junk Mail FilterSix, Aprior frequent item excavationBasic principle:(1) If a collection of items occurs frequently, all subsets of the item collection also appear frequently.(2) If the item collection does not appear frequently, all the superset of the item collection does not appear frequently.Advantages:(1) Easy to implement, an

, activating the back of the nerve layer, the final output layer of the nodes on the node on behalf of a variety of fractions, example to get the classification result of Class 1The same input is transferred to different nodes and the results are different because the respective nodes have different weights and biasThis is forward propagation.10. MarkovVideoMarkov Chains is made up of state and transitionsChestnuts, according to the phrase ' The quick brown fox jumps over the lazy dog ', to get

conjugate gradient method is not only one of the most useful methods to solve the large scale linear equations,is also one of the most effective algorithms for solving large-scale nonlinear optimization. In various optimization algorithms, the conjugate gradient method is very important. Its advantage is that the required storage capacity is small, has step convergence, high stability, and does not require

Recently learned about Python implementation of common machine learning algorithms on GitHubDirectory
First, linear regression
1. Cost function2. Gradient Descent algorithm3. Normalization of the mean value4. Final running result5, using the linear model in the Scikit-learn library to implement
S

the curve is above the Curve.The common convex functions are:
exponential function f (x) =ax;a>1
Negative logarithm function? logax;a>1,x>0
Two-time function of opening up
The decision of the convex function:1, If F is a first-order, x, y in any data domain satisfies F (y) ≥f (x) +f′ (x) (y?x)2. If f is a differentiable guide,Examples of convex optimization applications
SVM: which consists of max|w| Turn min (12?| W|2)

, example to get the classification result of Class 1The same input is transferred to different nodes and the results are different because the respective nodes have different weights and biasThis is forward propagation.10. MarkovVideoMarkov Chains is made up of state and transitionsChestnuts, according to the phrase ' The quick brown fox jumps over the lazy dog ', to get Markov chainStep, set each word to a state, and then calculate the probability of transitions between statesThis is the proba

paper is usually European-style distance, Pearson coefficient or cosine similarity.Assuming that a matrix A is established, the M*n matrix, the rows are all users, n is all items, each element of the matrix represents the user's rating of the item, then the item-based or user-based recommendation is to calculate the similarity of all columns or all rows. In real life, this matrix is very sparse.Topic: Recommend users to buy TOPN itemsThe Matrix C is a m*n matrix, each row represents each user,

nodes on the node on behalf of a variety of fractions, example to get the classification result of Class 1The same input is transferred to different nodes and the results are different because the respective nodes have different weights and biasThis is forward propagation.10. MarkovVideoMarkov Chains is made up of state and transitionsChestnuts, according to the phrase ' The quick brown fox jumps over the lazy dog ', to get Markov chainStep, set each word to a state, and then calculate the prob

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