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python sklearn learning notes

https://qaofficial.com/post/2019/05/04/24921-python-sklearn-learning-notes.html 2019-05-04
Preface: This article is a study note. sklearn introduction scikit-learn is a simple and effective tool for data mining and analysis.Relying on NumPy, SciPy and matplotlib. It mainly includes the following parts: from the function points:classificationRegressionClusteringDimensionality reductionModel selectionPreprocessing Divided from API modules:sklearn.base: Base classes and utility functionsklearn.cluster: Clusteringsklearn.cluster.bicluster: Biclusteringsklearn.covariance: Covariance Estimatorssklearn.model_selection: Model Selectionsklearn.datasets: Datasetssklearn.decomposition: Matrix Decompositionsklearn.dummy: Dummy estimatorssklearn.ensemble: Ensemble Methodssklearn.exceptions: Exceptions and warningssklearn.feature_extraction: Feature Extractionsklearn.feature_selection: Feature Selectionsklearn.gaussian_process: Gaussian Processessklearn.isotonic: Isotonic

sklearn, TensorFlow, keras model saving and reading

https://qaofficial.com/post/2019/05/04/24874-sklearn-tensorflow-keras-model-saving-and-reading.html 2019-05-04
1. sklearn Model Save and Read1. Preservation from sklearn.externals import joblib from sklearn import svm X = [[0, 0], [1, 1]] y = [0, 1] clf = svm.SVC() clf.fit(X, y) joblib.dump(clf, "train_model.m") 2, read clf = joblib.load("train_model.m") clf.predit([0,0]) #此处test_X为特征集 2. Save and read the tensorflow model (TensorFlow can only save variables instead of the entire network in this

【Scikit-learn] [Introduction] Six Functions of SCIKIT-LEARN

https://qaofficial.com/post/2019/05/04/24822-scikit-learn-introduction-six-functions-of-scikit-learn.html 2019-05-04
1. Introduction (1)SciPy, SciPy is an open source scientific computing toolkit based on Python. (2)Scikits, based on SciPy, has developed many branch versions for different application fields. They are collectively called Scikits, which means SciPy Toolkit (3) SCIKIT-LEARN is a branch version developed for machine learning based on SciPy. (4)Scikit-learn itself does not support deep learning. (5)Scikit-learn does not support GPU acceleration. Scikit-learn needs the support of other packages such as NumPy and SciPy, which is an open source framework developed by Python language for machine learning.

A Brief Introduction to the Lightweight Deep Learning Framework Keras

https://qaofficial.com/post/2019/05/04/24805-a-brief-introduction-to-the-lightweight-deep-learning-framework-keras.html 2019-05-04
徐海蛟教学 Keras是基于Theano的一个深度学习框架,它的设计参考了Torch,用Python语言编写,是一个高度模块化的神经网络库,支

ASP.NET is a common skill

https://qaofficial.com/post/2019/05/04/24866-asp.net-is-a-common-skill.html 2019-05-04
1. Database Access Performance Optimization Database Connection and Shutdown Access to database resources requires several operations, including creating connections, opening connections, and closing connections.These processes require multiple exchanges of information with the database to pass authentication, which consumes server resources.Connection Pool is provided in ASP.NET to improve the performance impact of opening and closing databases.The system places the user's database connection in the connection pool, takes it out when needed, withdraws the connection when closed, and waits for the next connection request.

Keras Learning Essay Based on Theano's Deep Learning Framework -10- Callback

https://qaofficial.com/post/2019/05/04/24862-keras-learning-essay-based-on-theanoamp#x27s-deep-learning-framework-10-callback.html 2019-05-04
Original Address: http://blog.csdn.net/niuwei22007/article/details/49229909 Callbacks (callback functions) are a set of functions used to be called at a specified stage during model training.You can view the internal information and statistical data of the model in the process of model training through the callback function.You can pass a callback function list to the fit () function, and then the related callback function can be called at the specified stage. 1, Callbacks base

Main Process of Installing and Using Keras on a New Ubuntu system

https://qaofficial.com/post/2019/05/04/24878-main-process-of-installing-and-using-keras-on-a-new-ubuntu-system.html 2019-05-04
First of all, you need to install the Ubuntu system, first make the installation USB stick, and follow the tutorial here: http://www.linuxidc.com/Linux/2016-04/130520.htm The first step is to install Universal USB Installer on Windows system. After searching, you can download it from this page provided by Baidu: http://rj.baidu.com/soft/detail/26320.html should be a non-toxic version. The second step is to make and install the USB flash drive. First, you need to download the

[reading notes] Keras Quick Start (1)

https://qaofficial.com/post/2019/05/04/24898-reading-notes-keras-quick-start-1.html 2019-05-04
"Keras Get Started Quickly" has a total of ten chapters. The part about deep learning can start directly from chapter 6. The recommendation in the installation book on Keras is to clone from Microsoft's server warehouse, install a git and add it to the environment variable, then clone from the git. As for the configuration part, Keras chooses one of CNTK,Tensorflow,Theano The ANO as the computing platform. In the book,

Fasttext text text classification

https://qaofficial.com/post/2019/05/03/24778-fasttext-text-text-classification.html 2019-05-03
1. Introduction 1, introduction fasttext is a word vector and text classification tool for facebook to open source. It will open source in 2016. The typical application scenario is "supervised text classification problem".Provides a simple and efficient text classification and Feature learning method with performance comparable to in-depth learning and faster speed. fastText combines the most successful concepts in natural language processing and machine learning.These include the use of word

Keras Introduction and Its Extensibility

https://qaofficial.com/post/2019/05/03/24804-keras-introduction-and-its-extensibility.html 2019-05-03
This article is reprinted from blog: https://blog.csdn.net/hewb14/article/details/53414068 Keras has good extensibility, on the one hand, because the interface is reserved at design time, and on the other hand, because the clear code structure allows you to have a lot of customization space.So here are a few examples of how to customize layers and various methods in Keras. 0、backend if you want to customize various layers and functions in Keras, you must use backend.