MachineLearning_Python/LinearRegression/LinearRegression.py/ Jump to cclaussModernize Python 2 code to get ready for Python 3 Latest commit202ff3bon Jan 4, 2018History 1contributor 116 lines (86 sloc)3.76 KB RawBlame #-*- coding: utf-8 -*- ...
python linear_regression_gui.py Project Components cost_function.py Contains the following functions: fcost(X, t_gt, weights): Calculates the cost for given features, target values, and weights. drev(X, t_gt, weights): Computes the gradient of the cost function. ...
链接:https://github.com/RRdmlearning/Machine-Learning-From-Scratch/tree/master/linear_regression 直接运行linear_regression.py即可
算法python实现 1.算法python代码 包含Normal Equations,批量梯度下降和随机梯度下降,这里的代码跟Logistic回归的代码类似 # -*- coding: utf-8 -*- import matplotlib.pyplot as plt import numpy as np class LinearRegression(object): def __init__(self): self._history_w = [] self._cost = [] def...
regression 基础 模型 使用Python实现长短时记忆网络(LSTM)的博客教程 网络pythonlstm博客教程 长短时记忆网络(Long Short-Term Memory,LSTM)是一种特殊类型的循环神经网络(RNN),专门设计用来解决序列数据中的长期依赖问题。本教程将介绍如何使用Python和PyTorch库实现一个简单的LSTM模型,并展示其在一个时间序列预测任务...
我们的目标和单变量线性回归问题中一样,是要找出使得代价函数最小的一系列参数。多变量线性回归的批量梯度下降算法为: 求导数后得到: (3)向量化计算 向量化计算可以加快计算速度,怎么转化为向量化计算呢? 在多变量情况下,损失函数可以写为: 对theta求导后得到: ...
The script shown in the steps below is main.py — which resides in the GitHub repository and is forked from the “Dive Into Deep learning” example repository. You can find code samples within the pytorch directory. For our regression example, you’ll need the following: Python 3 PyTorch ...
Tensorflow是目前非常流行的deeplearning框架,学习Tensorflow最好的方法是github上的tf项目https://github.com/tensorflow/tensorflow 或者阅读极客学院主导翻译的中文教程http://wiki.jikexueyuan.com/project/tensorflow-zh/how_tos/reading_data.html。 此处对tensorflow的基本语法不予赘述,直接贴上源码: ...
吴恩达机器学习(十三)—— ex5:Regularized Linear Regression and Bias v.s. Variance(MATLAB+Python),一、线性回归的正则化1.1可视化数据集1.2正则化线性回归的代价函数1.3线性回归梯度的正则化1.4拟合线性回归二、偏差和方差2.1学习曲线三、多项式回归3.1学习多项
Scikit-learn [57] is a well known Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised ML problems. It supports tools for classification, regression, clustering, dimensionality reduction, model selection and preprocessing. ...