├── README.md ├── basics ├── BiSection.py ├── Dijkstra.py ├── NewtonMethod.py ├── closure.py ├── primToMiniTree.py ├── topKByHeap.py └── topKByQuickSort.py ├── dl ├── FeedForwardNN.py ├── N-Gram模型.md ├── linear-regression-tensorflow.ipynb ├── mnistDataPre.py └── mnistSolver.ipynb ├── kaggle └── titanic │ ├── kaggle-titanic-predict-final.ipynb │ ├── test.csv │ └── train.csv ├── math ├── KKT.pdf └── condition-number.ipynb ├── matplotlib ├── .gitignore ├── AnatomyOfMatplotlib-Part0-Intro2NumPy.ipynb ├── AnatomyOfMatplotlib-Part1-Figures_Subplots_and_layouts.ipynb ├── AnatomyOfMatplotlib-Part2-Plotting_Methods_Overview.ipynb ├── AnatomyOfMatplotlib-Part3-HowToSpeakMPL.ipynb ├── AnatomyOfMatplotlib-Part4-Limits_Legends_and_Layouts.ipynb ├── AnatomyOfMatplotlib-Part5-Artists.ipynb ├── AnatomyOfMatplotlib-Part6-mpl_toolkits.ipynb ├── COPYRIGHT.txt ├── Matplot绘图.md ├── README.md ├── Test Install.ipynb ├── assets │ ├── 5.png │ └── bivariate_normal.npy ├── examples │ ├── bar_example.py │ ├── contour_example.py │ ├── example_utils.py │ ├── fill_example.py │ ├── imshow_example.py │ ├── pcolor_example.py │ ├── plot_example.py │ ├── scatter_example.py │ ├── statistical_example.py │ └── vector_example.py ├── exercises │ ├── 1.1-subplots_and_basic_plotting.py │ ├── 2.1-bar_and_fill_between.py │ ├── 2.2-vmin_vmax_imshow_and_colorbars.py │ ├── 3.1-colors.py │ ├── 3.2-markers.py │ ├── 3.3-properties.py │ ├── 3.4-arrows.py │ ├── 4.1-legends_and_scaling.py │ ├── 4.2-spines_ticks_and_subplot_spacing.py │ └── 5.1-goldstar.py ├── images │ ├── bar_example.png │ ├── contour_example.png │ ├── exercise_1-1.png │ ├── exercise_2.1-bar_and_fill_between.png │ ├── exercise_2.2-vmin_vmax_imshow_and_colorbars.png │ ├── exercise_4-1.png │ ├── exercise_4-2.png │ ├── figure_axes_axis_labeled.png │ ├── fill_example.png │ ├── imshow_example.png │ ├── pcolor_example.png │ ├── plot_example.png │ ├── scatter_example.png │ ├── statistical_example.png │ └── vector_example.png ├── plot_demo.svg └── solutions │ ├── 1.1-subplots_and_basic_plotting.py │ ├── 2.1-bar_and_fill_between.py │ ├── 2.2-vmin_vmax_imshow_and_colorbars.py │ ├── 3.1-colors.py │ ├── 3.2-markers.py │ ├── 3.3-properties.py │ ├── 3.4-arrows.py │ ├── 4.1-legends_and_scaling.py │ ├── 4.2-spines_ticks_and_subplot_spacing.py │ └── 5.1-goldstar.py ├── ml ├── GMM.ipynb ├── bayes │ ├── .ipynb_checkpoints │ │ └── spellcheck-checkpoint.ipynb │ ├── bigword.txt │ └── spellcheck.ipynb ├── decision-tree │ ├── decision-tree-classification.ipynb │ ├── decision-tree-graphviz.ipynb │ └── decision-tree-regression.ipynb ├── img │ ├── 1572352181475.png │ ├── 1572352244069.png │ ├── 1572352632221.png │ ├── 1573699352403.png │ ├── 1573699993369.png │ ├── 1573700241030.png │ ├── 1573700535446.png │ ├── 1573710321269.png │ ├── 1573710387302.png │ ├── 1573710525645.png │ ├── 1573710732713.png │ ├── 1573711009411.png │ ├── 1574042311831.png │ ├── 1574262159169.png │ ├── 1574305127247.png │ ├── 1574305178493.png │ ├── 1574305221924.png │ ├── 1574305256595.png │ ├── 1574305291988.png │ ├── 1574305423823.png │ └── 1574305699838.png ├── linear-regression.ipynb ├── logistic-regression.ipynb ├── pca │ ├── PCA.ipynb │ ├── SVD.ipynb │ └── tsne.md ├── xgboost-basics.ipynb ├── 入门机器学习，这是我见过的最直白讲解.md └── 梯度提升树(GBDT)完整教程 V1.1 发布.pdf ├── numpy ├── matric searching methods.md ├── matric searching methods.pdf └── numpy_index_slice.ipynb ├── pandas ├── pandas_all.ipynb ├── pandas_column_number.ipynb ├── pandas_column_sort.ipynb ├── pandas_drop_duplicates.ipynb ├── pandas_loc.ipynb ├── pd_data_save.xls ├── play_pandas_merge_DataFrame.ipynb ├── play_pandas_mutiIndex_pivot_sort.ipynb ├── play_pandas_series.ipynb ├── play_pd_DataFrame.ipynb ├── to_csv中文乱码.ipynb └── top_missing_fun.ipynb ├── python ├── .ipynb_checkpoints │ ├── Untitled-checkpoint.ipynb │ ├── Untitled1-checkpoint.ipynb │ └── Zip-1-checkpoint.ipynb ├── 24式加速你的Python.ipynb ├── Collections.md ├── Python63个内置函数.md ├── Type Checking.md ├── __pycache__ │ └── decorator.cpython-37.pyc ├── batch_file_rename.md ├── decorator.py ├── generatorFun.py ├── highOrderFun.py ├── isinequal.md ├── itertools-groupby-example.py ├── itertools.md ├── make_dir_if_not_exist.md ├── python-time.md ├── zip.ipynb ├── 列表生成式.md ├── 太好了，一分钟带你分清Python的模块、包和库.md └── 查看文件信息.md ├── spark ├── 1.png ├── 1小时入门Spark之RDD编程.ipynb ├── 2.png ├── 20分钟学会DBSCAN聚类算法.ipynb ├── 2小时入门SparkSQL编程.ipynb ├── 2小时入门Spark之MLlib.ipynb ├── 3.png ├── 3小时Java入门.ipynb ├── 3小时Python入门.ipynb ├── 3小时Scala入门.ipynb ├── 4.png ├── 5.png ├── 6.png ├── 7.png ├── 7个Spark编程练习题.ipynb ├── 8.png ├── 9.png ├── Bag.java ├── DBSCAN算法的Spark实现.ipynb ├── Digraph.java ├── DirectedCycle.java ├── Java容器.gif ├── Java异常体系.png ├── PythonAiRoad-master │ ├── .ipynb_checkpoints │ │ ├── 20分钟学会DBSCAN聚类算法-checkpoint.ipynb │ │ ├── 30分钟学会pyechart数据可视化-checkpoint.ipynb │ │ ├── 3小时入门numpy,pandas,matplotlib-checkpoint.ipynb │ │ ├── DBSCAN算法的Spark实现-checkpoint.ipynb │ │ └── commit-to-github-checkpoint.ipynb │ ├── 20分钟学会DBSCAN聚类算法.ipynb │ ├── 24式加速你的Python.ipynb │ ├── 30分钟学会pyechart数据可视化.ipynb │ ├── 3小时Python入门.ipynb │ ├── 3小时入门numpy,pandas,matplotlib.ipynb │ ├── DBSCAN算法的Spark实现.ipynb │ ├── commit-to-github.ipynb │ └── data │ │ ├── DBSCAN2个算法参数.png │ │ ├── DBSCAN3种点的类型.png │ │ ├── DBSCAN4种点的关系.png │ │ ├── DBSCAN核心思想.png │ │ ├── DBSCAN算法步骤.png │ │ ├── DBSCAN聚类算法.pptx │ │ ├── class-relationship-diagram.png │ │ ├── dbscan核心算法的输入.png │ │ ├── dbscan核心算法的输出.png │ │ ├── moon_points.csv │ │ ├── sklearn的DBSCAN聚类结果.png │ │ ├── ~$DBSCAN聚类算法.pptx │ │ ├── 折线图示范.html │ │ ├── 散点图示范.html │ │ └── 柱形图示范.html ├── RDD,DataFrame,DataSet对比.jpg ├── aagit.ipynb ├── data.txt ├── data │ ├── DBSCAN2个算法参数.png │ ├── DBSCAN3种点的类型.png │ ├── DBSCAN4种点的关系.png │ ├── DBSCAN核心思想.png │ ├── DBSCAN算法步骤.png │ ├── DBSCAN聚类算法.pptx │ ├── class-relationship-diagram.png │ ├── dbscan核心算法的输入.png │ ├── dbscan核心算法的输出.png │ ├── iris.csv │ ├── mllib │ │ ├── als │ │ │ ├── sample_movielens_ratings.txt │ │ │ └── test.data │ │ ├── gmm_data.txt │ │ ├── images │ │ │ ├── license.txt │ │ │ ├── origin │ │ │ │ ├── kittens │ │ │ │ │ ├── 29.5.a_b_EGDP022204.jpg │ │ │ │ │ ├── 54893.jpg │ │ │ │ │ ├── DP153539.jpg │ │ │ │ │ ├── DP802813.jpg │ │ │ │ │ └── not-image.txt │ │ │ │ ├── license.txt │ │ │ │ └── multi-channel │ │ │ │ │ ├── BGRA.png │ │ │ │ │ ├── BGRA_alpha_60.png │ │ │ │ │ ├── chr30.4.184.jpg │ │ │ │ │ └── grayscale.jpg │ │ │ └── partitioned │ │ │ │ ├── cls=kittens │ │ │ │ ├── date=2018-01 │ │ │ │ │ ├── 29.5.a_b_EGDP022204.jpg │ │ │ │ │ └── not-image.txt │ │ │ │ └── date=2018-02 │ │ │ │ │ ├── 54893.jpg │ │ │ │ │ ├── DP153539.jpg │ │ │ │ │ └── DP802813.jpg │ │ │ │ └── cls=multichannel │ │ │ │ ├── date=2018-01 │ │ │ │ ├── BGRA.png │ │ │ │ └── BGRA_alpha_60.png │ │ │ │ └── date=2018-02 │ │ │ │ ├── chr30.4.184.jpg │ │ │ │ └── grayscale.jpg │ │ ├── iris_libsvm.txt │ │ ├── kmeans_data.txt │ │ ├── pagerank_data.txt │ │ ├── pic_data.txt │ │ ├── ridge-data │ │ │ └── lpsa.data │ │ ├── sample_binary_classification_data.txt │ │ ├── sample_fpgrowth.txt │ │ ├── sample_isotonic_regression_libsvm_data.txt │ │ ├── sample_kmeans_data.txt │ │ ├── sample_lda_data.txt │ │ ├── sample_lda_libsvm_data.txt │ │ ├── sample_libsvm_data.txt │ │ ├── sample_linear_regression_data.txt │ │ ├── sample_movielens_data.txt │ │ ├── sample_multiclass_classification_data.txt │ │ ├── sample_svm_data.txt │ │ └── streaming_kmeans_data_test.txt │ ├── moon_points.csv │ ├── sklearn的DBSCAN聚类结果.png │ ├── ~$DBSCAN聚类算法.pptx │ ├── 折线图示范.html │ ├── 散点图示范.html │ └── 柱形图示范.html ├── getlen.py ├── imagedata │ ├── Java图片.jpeg │ ├── Python与算法之美Logo.png │ ├── java解释型.png │ └── logo.jpg ├── java解释型.png ├── jointables.png ├── resources │ ├── employees.json │ ├── full_user.avsc │ ├── iris.csv │ ├── kv1.txt │ ├── people.csv │ ├── people.json │ ├── people.txt │ ├── user.avsc │ ├── users.avro │ ├── users.orc │ └── users.parquet ├── scala数据类型.png ├── secondary_sort.png ├── sortwithindex.png ├── spark-ml-linear-regression-demo.py ├── spark-ml-linear_regression.md ├── test.scala ├── test.txt ├── 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