I First of all, the library to be introduced
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
matplotlib.rcParams['font.sans-serif']=['SimHei']#The purpose of displaying Chinese in bold is that the axis label and title of the figure made by matplotlib can be expressed in Chinese
II Import file
Posted by TreColl on Mon, 09 May 2022 23:22:18 +0300
Lazy looking for the smallest data set on UCI, the data is about the speed and angle of the container crane, to judge its strength (I don't know the crane ah ah ah)
Although you don't understand it, it doesn't prevent you from writing code classifications. Obviously, markers are power, divided into 0.3, 0.5, and 0.7. Specific model learning or ...
Posted by Ansel_Tk1 on Mon, 09 May 2022 19:09:55 +0300
Image recognition experiment using ResNet18 residual network model of CIFAR10 data set - accuracy of 90% (detailed notes attached)
Auther:Yuandong Li Date: 2020/11/17 Welcome to my github: Li-Y-D The source code of this experiment and the code file in Jupyter Notebook format: Image-classification-CIFAR10-ResNet18
I Test requirements:
Use Jup ...
Posted by cretam on Sat, 07 May 2022 04:50:48 +0300
Force tensor to this data type tf. Cast (tensor name, dtype = data type)
Calculate the maximum and minimum values of elements in the tensor dimension
tf. reduce_ Max (tensor name) tf. reduce_ Min (tensor name)
import tensorflow as tf
x1 = tf.constant([1, 2, 3], dtype=tf.float64)
x2 = tf.cast(x1, tf.int32)
Posted by PHPycho on Fri, 06 May 2022 13:41:13 +0300
Anomaly detection is to find data points that deviate from the specification. In other words, these points do not conform to the expected pattern. Outliers and exceptions are terms used to describe abnormal data. Anomaly detection is important in all areas because it provides valuable and actionable insights. For example, abnormalities in MRI s ...
Posted by mattsoftnet on Fri, 06 May 2022 13:12:08 +0300
k-nearest neighbors (k-NN) is a basic classification and regression method. Input: the feature vector of the instance, corresponding to the points in the feature space; Output: The class of the instance. When classifying, the new instance is predicted by majority voting according to the class of its k nearest neighbors of the training instance. ...
Posted by kkessler on Thu, 05 May 2022 17:31:35 +0300
Time series model ARIMA
Application process of ARIMA model
The stationarity is identified according to the scatter diagram, autocorrelation function and partial autocorrelation function diagram of time series.
The non-stationary time series data are smoothed. Until the processed autocorrelation function and partial autocorrel ...
Posted by flunn on Thu, 05 May 2022 02:36:54 +0300
Classical network ResNet
1 Brief description
Networks such as GoogleNet and VGG have demonstrated that deeper networks can abstract more expressive features and thus achieve stronger classification capabilities. In a deep network, with the increase of the network depth, the resolution of the feature map output by each layer is mainly reduced ...
Posted by RDx321 on Wed, 04 May 2022 17:58:02 +0300
NumPy supports a large number of dimensional array and matrix operations. It is a Python library for array operations.
This article is included in Pre machine learning series.
1, Python Basics
Let's first consolidate the basics of Python. Python has six standard data types: Number, String, List, Tuple, Set, and Dictionary. Of which: Immutabl ...
Posted by lanbor on Tue, 03 May 2022 23:33:06 +0300