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deliver\nfast and easy model prototyping.\n\nzeta-learn aims to provide an extensive understanding of machine learning through\nthe use of straightforward algorithms and readily implemented examples making\nit a useful resource for researchers and students.\n\n * **Documentation:** https://zeta-learn.com\n * **Python versions:** 3.5 and above\n * **Free software:** MIT license\n\nDependencies\n------------\n - numpy \u003e= 1.15.0\n - matplotlib \u003e= 2.0.0\n\nFeatures\n--------\n - Keras like Sequential API for building models.\n - Built on Numpy and Matplotlib.\n - Examples folder with readily implemented machine learning models.\n\nInstall\n-------\n  - pip install ztlearn\n\nExamples\n--------\n\nPrincipal Component Analysis (PCA)\n##################################\n\n`DIGITS Dataset - PCA \u003chttps://github.com/jefkine/zeta-learn/blob/master/examples/digits/digits_pca.py\u003e`_\n=====================\n.. image:: /examples/plots/results/pca/digits_pca.png\n      :align: center\n      :alt: digits pca\n\n\n`MNIST Dataset - PCA \u003chttps://github.com/jefkine/zeta-learn/blob/master/examples/mnist/mnist_pca.py\u003e`_\n====================\n.. image:: /examples/plots/results/pca/mnist_pca.png\n      :align: center\n      :alt: mnist pca\n\nKMEANS\n######\n\n`K-Means Clustering (4 Clusters) \u003chttps://github.com/jefkine/zeta-learn/blob/master/examples/clusters/kmeans_cluestering.py\u003e`_\n================================\n.. image:: /examples/plots/results/kmeans/k_means_4_clusters.png\n      :align: center\n      :alt: k-means (4 clusters)\n\nConvolutional Neural Network (CNN)\n##################################\n\n\n`DIGITS Dataset Model Summary \u003chttps://github.com/jefkine/zeta-learn/blob/master/examples/digits/digits_cnn.py\u003e`_\n=============================\n.. code:: html\n\n  DIGITS CNN\n\n  Input Shape: (1, 8, 8)\n  +---------------------+---------+--------------+\n  ¦ LAYER TYPE          ¦  PARAMS ¦ OUTPUT SHAPE ¦\n  +---------------------+---------+--------------+\n  ¦ Conv2D              ¦     320 ¦   (32, 8, 8) ¦\n  ¦ Activation: RELU    ¦       0 ¦   (32, 8, 8) ¦\n  ¦ Dropout             ¦       0 ¦   (32, 8, 8) ¦\n  ¦ BatchNormalization  ¦   4,096 ¦   (32, 8, 8) ¦\n  ¦ Conv2D              ¦  18,496 ¦   (64, 8, 8) ¦\n  ¦ Activation: RELU    ¦       0 ¦   (64, 8, 8) ¦\n  ¦ MaxPooling2D        ¦       0 ¦   (64, 7, 7) ¦\n  ¦ Dropout             ¦       0 ¦   (64, 7, 7) ¦\n  ¦ BatchNormalization  ¦   6,272 ¦   (64, 7, 7) ¦\n  ¦ Flatten             ¦       0 ¦     (3,136,) ¦\n  ¦ Dense               ¦ 803,072 ¦       (256,) ¦\n  ¦ Activation: RELU    ¦       0 ¦       (256,) ¦\n  ¦ Dropout             ¦       0 ¦       (256,) ¦\n  ¦ BatchNormalization  ¦     512 ¦       (256,) ¦\n  ¦ Dense               ¦   2,570 ¦        (10,) ¦\n  +---------------------+---------+--------------+\n\n  TOTAL PARAMETERS: 835,338\n\nDIGITS Dataset Model Results\n============================\n.. image:: /examples/plots/results/cnn/digits_cnn_tiled_results.png\n      :align: center\n      :alt: digits cnn results tiled\n\nDIGITS Dataset Model Loss\n=========================\n.. image:: /examples/plots/results/cnn/digits_cnn_loss_graph.png\n      :align: center\n      :alt: digits model loss\n\nDIGITS Dataset Model Accuracy\n=============================\n.. image:: /examples/plots/results/cnn/digits_cnn_accuracy_graph.png\n      :align: center\n      :alt: digits model accuracy\n\n`MNIST Dataset Model Summary \u003chttps://github.com/jefkine/zeta-learn/blob/master/examples/mnist/mnist_cnn.py\u003e`_\n============================\n.. code:: html\n\n  MNIST CNN\n\n  Input Shape: (1, 28, 28)\n  +---------------------+------------+--------------+\n  ¦ LAYER TYPE          ¦     PARAMS ¦ OUTPUT SHAPE ¦\n  +---------------------+------------+--------------+\n  ¦ Conv2D              ¦        320 ¦ (32, 28, 28) ¦\n  ¦ Activation: RELU    ¦          0 ¦ (32, 28, 28) ¦\n  ¦ Dropout             ¦          0 ¦ (32, 28, 28) ¦\n  ¦ BatchNormalization  ¦     50,176 ¦ (32, 28, 28) ¦\n  ¦ Conv2D              ¦     18,496 ¦ (64, 28, 28) ¦\n  ¦ Activation: RELU    ¦          0 ¦ (64, 28, 28) ¦\n  ¦ MaxPooling2D        ¦          0 ¦ (64, 27, 27) ¦\n  ¦ Dropout             ¦          0 ¦ (64, 27, 27) ¦\n  ¦ BatchNormalization  ¦     93,312 ¦ (64, 27, 27) ¦\n  ¦ Flatten             ¦          0 ¦    (46,656,) ¦\n  ¦ Dense               ¦ 11,944,192 ¦       (256,) ¦\n  ¦ Activation: RELU    ¦          0 ¦       (256,) ¦\n  ¦ Dropout             ¦          0 ¦       (256,) ¦\n  ¦ BatchNormalization  ¦        512 ¦       (256,) ¦\n  ¦ Dense               ¦      2,570 ¦        (10,) ¦\n  +---------------------+------------+--------------+\n\n  TOTAL PARAMETERS: 12,109,578\n\nMNIST Dataset Model Results\n===========================\n.. image:: /examples/plots/results/cnn/mnist_cnn_tiled_results.png\n      :align: center\n      :alt: mnist cnn results tiled\n\n\nRegression\n##########\n\n`Linear Regression \u003chttps://github.com/jefkine/zeta-learn/blob/master/examples/boston/boston_linear_regression.py\u003e`_\n==================\n.. image:: /examples/plots/results/regression/linear_regression.png\n      :align: center\n      :alt: linear regression\n\n`Polynomial Regression \u003chttps://github.com/jefkine/zeta-learn/blob/master/examples/boston/boston_polynomial_regression.py\u003e`_\n======================\n.. image:: /examples/plots/results/regression/polynomial_regression.png\n      :align: center\n      :alt: polynomial regression\n\n`Elastic Regression \u003chttps://github.com/jefkine/zeta-learn/blob/master/examples/boston/boston_elastic_regression.py\u003e`_\n=================\n.. image:: /examples/plots/results/regression/elastic_regression.png\n      :align: center\n      :alt: elastic 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