ztlearn 1.1.5
Minimalistic Python Machine Learning Toolkit.
License: MIT
Requires Python: >=3.5.0


zeta-learn
----------
zeta-learn is a minimalistic python machine learning library designed to deliver
fast and easy model prototyping.

zeta-learn aims to provide an extensive understanding of machine learning through
the use of straightforward algorithms and readily implemented examples making
it a useful resource for researchers and students.

 * **Documentation:** https://zeta-learn.com
 * **Python versions:** 3.5 and above
 * **Free software:** MIT license

Dependencies
------------
 - numpy >= 1.15.0
 - matplotlib >= 2.0.0

Features
--------
 - Keras like Sequential API for building models.
 - Built on Numpy and Matplotlib.
 - Examples folder with readily implemented machine learning models.

Install
-------
  - pip install ztlearn

Examples
--------

Principal Component Analysis (PCA)
##################################

`DIGITS Dataset - PCA <https://github.com/jefkine/zeta-learn/blob/master/examples/digits/digits_pca.py>`_
=====================
.. image:: /examples/plots/results/pca/digits_pca.png
      :align: center
      :alt: digits pca


`MNIST Dataset - PCA <https://github.com/jefkine/zeta-learn/blob/master/examples/mnist/mnist_pca.py>`_
====================
.. image:: /examples/plots/results/pca/mnist_pca.png
      :align: center
      :alt: mnist pca

KMEANS
######

`K-Means Clustering (4 Clusters) <https://github.com/jefkine/zeta-learn/blob/master/examples/clusters/kmeans_cluestering.py>`_
================================
.. image:: /examples/plots/results/kmeans/k_means_4_clusters.png
      :align: center
      :alt: k-means (4 clusters)

Convolutional Neural Network (CNN)
##################################


`DIGITS Dataset Model Summary <https://github.com/jefkine/zeta-learn/blob/master/examples/digits/digits_cnn.py>`_
=============================
.. code:: html

  DIGITS CNN

  Input Shape: (1, 8, 8)
  +---------------------+---------+--------------+
  ¦ LAYER TYPE          ¦  PARAMS ¦ OUTPUT SHAPE ¦
  +---------------------+---------+--------------+
  ¦ Conv2D              ¦     320 ¦   (32, 8, 8) ¦
  ¦ Activation: RELU    ¦       0 ¦   (32, 8, 8) ¦
  ¦ Dropout             ¦       0 ¦   (32, 8, 8) ¦
  ¦ BatchNormalization  ¦   4,096 ¦   (32, 8, 8) ¦
  ¦ Conv2D              ¦  18,496 ¦   (64, 8, 8) ¦
  ¦ Activation: RELU    ¦       0 ¦   (64, 8, 8) ¦
  ¦ MaxPooling2D        ¦       0 ¦   (64, 7, 7) ¦
  ¦ Dropout             ¦       0 ¦   (64, 7, 7) ¦
  ¦ BatchNormalization  ¦   6,272 ¦   (64, 7, 7) ¦
  ¦ Flatten             ¦       0 ¦     (3,136,) ¦
  ¦ Dense               ¦ 803,072 ¦       (256,) ¦
  ¦ Activation: RELU    ¦       0 ¦       (256,) ¦
  ¦ Dropout             ¦       0 ¦       (256,) ¦
  ¦ BatchNormalization  ¦     512 ¦       (256,) ¦
  ¦ Dense               ¦   2,570 ¦        (10,) ¦
  +---------------------+---------+--------------+

  TOTAL PARAMETERS: 835,338

DIGITS Dataset Model Results
============================
.. image:: /examples/plots/results/cnn/digits_cnn_tiled_results.png
      :align: center
      :alt: digits cnn results tiled

DIGITS Dataset Model Loss
=========================
.. image:: /examples/plots/results/cnn/digits_cnn_loss_graph.png
      :align: center
      :alt: digits model loss

DIGITS Dataset Model Accuracy
=============================
.. image:: /examples/plots/results/cnn/digits_cnn_accuracy_graph.png
      :align: center
      :alt: digits model accuracy

`MNIST Dataset Model Summary <https://github.com/jefkine/zeta-learn/blob/master/examples/mnist/mnist_cnn.py>`_
============================
.. code:: html

  MNIST CNN

  Input Shape: (1, 28, 28)
  +---------------------+------------+--------------+
  ¦ LAYER TYPE          ¦     PARAMS ¦ OUTPUT SHAPE ¦
  +---------------------+------------+--------------+
  ¦ Conv2D              ¦        320 ¦ (32, 28, 28) ¦
  ¦ Activation: RELU    ¦          0 ¦ (32, 28, 28) ¦
  ¦ Dropout             ¦          0 ¦ (32, 28, 28) ¦
  ¦ BatchNormalization  ¦     50,176 ¦ (32, 28, 28) ¦
  ¦ Conv2D              ¦     18,496 ¦ (64, 28, 28) ¦
  ¦ Activation: RELU    ¦          0 ¦ (64, 28, 28) ¦
  ¦ MaxPooling2D        ¦          0 ¦ (64, 27, 27) ¦
  ¦ Dropout             ¦          0 ¦ (64, 27, 27) ¦
  ¦ BatchNormalization  ¦     93,312 ¦ (64, 27, 27) ¦
  ¦ Flatten             ¦          0 ¦    (46,656,) ¦
  ¦ Dense               ¦ 11,944,192 ¦       (256,) ¦
  ¦ Activation: RELU    ¦          0 ¦       (256,) ¦
  ¦ Dropout             ¦          0 ¦       (256,) ¦
  ¦ BatchNormalization  ¦        512 ¦       (256,) ¦
  ¦ Dense               ¦      2,570 ¦        (10,) ¦
  +---------------------+------------+--------------+

  TOTAL PARAMETERS: 12,109,578

MNIST Dataset Model Results
===========================
.. image:: /examples/plots/results/cnn/mnist_cnn_tiled_results.png
      :align: center
      :alt: mnist cnn results tiled


Regression
##########

`Linear Regression <https://github.com/jefkine/zeta-learn/blob/master/examples/boston/boston_linear_regression.py>`_
==================
.. image:: /examples/plots/results/regression/linear_regression.png
      :align: center
      :alt: linear regression

`Polynomial Regression <https://github.com/jefkine/zeta-learn/blob/master/examples/boston/boston_polynomial_regression.py>`_
======================
.. image:: /examples/plots/results/regression/polynomial_regression.png
      :align: center
      :alt: polynomial regression

`Elastic Regression <https://github.com/jefkine/zeta-learn/blob/master/examples/boston/boston_elastic_regression.py>`_
=================
.. image:: /examples/plots/results/regression/elastic_regression.png
      :align: center
      :alt: elastic regression