文件名称：UHMachineLearning：实现机器学习算法的Jupyter Notebooks的集合
文件大小：120.29MB
文件格式：ZIP
更新时间：2024-03-05 23:26:02
JupyterNotebook
机器学习 实现以下机器学习算法的Jupyter Notebook集合：逻辑回归，支持向量机，决策树，随机森林，集成学习，聚类，降维和神经网络（包括简单的前馈网络和卷积神经网络）。 还包括基本Python编程，线性代数和TensorFlow的教程。
【文件预览】：
UHMachineLearning-master
----Lab1()
--------life_satisfaction_vs_gdp_per_capita.csv(814B)
--------Lab_LinearAlgebra.ipynb(77KB)
--------readme.md(86B)
----Lab5()
--------classification_report.PNG(6KB)
--------DecisionTrees_example.ipynb(62KB)
--------iris(514B)
--------tree.png(62KB)
--------DecisionTrees_LabExercise.ipynb(271KB)
--------errorcurve.png(15KB)
--------errorcurve_realdata.png(19KB)
--------readme.md(87B)
--------iris.pdf(16KB)
--------photo.png(1.64MB)
----Lab4()
--------training_data.csv(196KB)
--------SVM_Example2_Moon.ipynb(28KB)
--------Valentine.jpg(177KB)
--------Lab4_SVM_LabExercise.ipynb(469KB)
--------SVM_Example1_FaceRecognition.ipynb(240KB)
--------ClassificationReport.PNG(12KB)
--------1_facies.PNG(47KB)
--------faciesplot.py(5KB)
--------readme.md(210B)
----Lab9()
--------TF_LinearRegression.ipynb(90KB)
--------TF_ComputationGraph.ipynb(19KB)
--------TF_LogisticRegression.ipynb(172KB)
--------readme.md(53B)
--------TF_DNN_example.ipynb(80KB)
----Lab7()
--------GBlearning2.png(185KB)
--------readme(35B)
--------EnsembleLearning_LabExercise.ipynb(38KB)
--------DecisionBoundary.PNG(924KB)
--------GBlearning.PNG(294KB)
--------Learning_Rate.png(768KB)
--------photo.png(794KB)
----Lab6()
--------RandomForest_example.ipynb(133KB)
--------ClassificationReport.PNG(6KB)
--------RandomForest_LabExercise.ipynb(21KB)
--------FeatureImportance.png(9KB)
--------errorcurves_25_ExtraTrees.png(20KB)
--------readme.md(67B)
--------errorcurves_25.png(20KB)
--------photo.png(1.86MB)
----Lab2()
--------Lab2_ConvergeCurveSGD.png(16KB)
--------Lab2_GradientDescent.ipynb(73KB)
--------Lab2_SearchPathBGD.png(13KB)
--------readme.md(88B)
--------Lab2_SearchPathBGDSGD.png(17KB)
--------Lab2_ConvergeCurveBGD.png(11KB)
----Lab3()
--------Lab3_LogisticRegression_example.ipynb(115KB)
--------iris.png(2.17MB)
--------Lab3_LogisticRegression_LabExercise.ipynb(21KB)
--------TA_AK.png(5.3MB)
--------readme.md(191B)
--------photo.png(1.64MB)
----mnist-original.mat(52.87MB)
----Lab11()
--------YMagneticDataMaps.npy(32KB)
--------poto.png(3.27MB)
--------Week11_Keras_CNN_lab.ipynb(58KB)
--------Week11_Keras_example_CNNs.ipynb(82KB)
--------Week11_Keras_example_FeedforwardNN.ipynb(39KB)
--------InclinationDictionary.npy(3KB)
--------readme.md(57B)
----LICENSE(1KB)
----Traces_qc.mat(62.17MB)
----Lab12()
--------Copy_of_01_Seismic_Fault_Classification_DeepLearning_Synthetic.ipynb(1.09MB)
--------readme.md(118B)
----README.md(428B)
----Lab8()
--------well_trajectory.csv(8KB)
--------XRF_dataset.csv(34KB)
--------KMeans_exercise.ipynb(1.95MB)
--------KMeans_Example.ipynb(156KB)
--------PCA_example.ipynb(96KB)
--------readme.md(87B)
--------Hall.PNG(249KB)
----Lab10()
--------TF_LabExercise.ipynb(21KB)
--------readme.md(79B)
--------photo.png(1.64MB)
--------TF_DNN_example.ipynb(80KB)
----Lab0()
--------1_Introduction to Jupyter Notebook.ipynb(19KB)
--------2_Introduction to basic Python programming.ipynb(23KB)
--------readme.md(120B)
----LectureNotes()
--------Week2_Lecture_Optimization.pdf(1.14MB)
--------Week5_SVM.pdf(1.57MB)
--------Week13_NN_PartI.pdf(2.21MB)
--------Week13_NN_PartII.pdf(1011KB)
--------Week14_CNN.pdf(1.31MB)
--------Week3_Concepts.pdf(2.59MB)
--------Week11_UnsupervisedLearning.pdf(1.31MB)
--------Week8_EnsembleLearning.pdf(2.37MB)
--------Week1_lecture2_LineAlgebra.pdf(828KB)
--------Week6_DecisionTrees.pdf(1.17MB)
--------Week2_lab_AzureJupyter.pdf(574KB)
--------Week4_Lecture_LogisticRegression.pdf(1.88MB)
--------Week1_lecture1_Introduction.pdf(3.31MB)
--------Week7_RandomForests.pdf(1022KB)
--------Week12_TensorFlow.pdf(879KB)
--------Week10_Review.pdf(3.6MB)
--------readme.md(104B)