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Minimalistic Python Machine Learning Toolkit.

Project description


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

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