deploy machine learning model flask

Based on the literal Relevance Model of Baidu keyword search recommendation tool, this article introduces the specific design and implementation of a machine learning task. Including target setting, training data preparation, feature selection and filtering, and model training and optimization. This

, optimization model, decision model and so on. According to the understanding of the model structure: White box model, gray box model, and black box model. Two. System identification in scientific research and engineering practic

[Ai refining] machine learning 051-bag of Vision Model + extreme random forest to build an image classifier (Python library and version number used in this article: Python 3.6, numpy 1.14, scikit-learn 0.19, matplotlib 2.2) Bag of visual words (bovw) comes from bag of words (BOW) in natural language processing, for more information, see my blog [ai refining]

Connect Azure machine Learning (iii) to create an Azure machine learning experiment, the next step is to really publish the predictive model of Azure machine learning as a Web service.

(such as GBDT) are typical of the method, today mainly talk about the gradient boosting method (this is a little different from the traditional boosting) some mathematical basis, With this mathematical basis, the application above can be seen Freidman gradient boosting machine.This article requires the reader to learn basic college mathematics, as well as the basic machine learning concepts of classificati

each match point between the cost and minimum. The results of the match are as follows (Figure III): (Figure III) The above method does not use machine learning, the other part of the search is not an easy thing, because the first to approximate the location of the component, so this method also has shortcomings, but the idea of the deformed part can be used as a feature, and then look at Pedro's second a

(such as GBDT) are typical of the method, today mainly talk about the gradient boosting method (this is a little different from the traditional boosting) some mathematical basis, With this mathematical basis, the application above can be seen Freidman gradient boosting machine.This article requires the reader to learn basic college mathematics, as well as the basic machine learning concepts of classificati

All machine learning models are defective (by John Langford) Attempts to abstract and study machine learning are within some given framework or mathematical model. it turns out that all of these models are significantly flawed for the purpose of studying

this time corresponding to the x=0, so x=0 is our ultimate goal . if the initial position of the x0>0 begins to drop, the next value is x1=x0-2*alpha*x0, which is closer to the origin than the x0;such as x0=2,alpha=0.1, then x1=2-2*0.1*2=1.6. Obviously x=1.6, the loss function is smaller than the x=2, and we are a step closer to the goal.if the initial position of the xsuch as x0=-2,alpha=0.1, then X1=-2-2*0.1* (-2) =-1.6. Obviously x=-1.6, the loss function is smaller than the x=-2, and we're

This lesson mainly describes the processing of linear models. Including: 1. Input Representation) 2. Linear Classification) 3. Linear Regression) 4. nonlinear transformation) The author believes that to test the availability of a model, it is to use real data to do a good job. To explain how to apply linear models, the author uses linear models to solve the problem of post office data identification: Because different people have different writing ha

01 Brief Introduction The probability graph model is the product of the combination of graph theory and probability theory. It was created by Judea Pearl, a famous one. I like the probability graph model tool very much, it is a powerful multi-variable and visualized modeling tool for variable relations, mainly including two major directions: undirected graph model