NeuPy

Neural Networks in Python

  • Articles
  • Tutorials
  • Documentation
  • Cheat sheet
  • Model Zoo
  • 2019 Jun 10 Earthquakes in the Landscape of Neural Network

    In this article, I want to direct your attention to the less known properties of one, quite famous, technique in the deep learning. I want to show you how beautiful and interesting could be a concept that typically left behind because of all more exciting ideas in this area.


    Tags: visualization, backpropagation
  • 2017 Dec 13 The Art of SOFM

    It's quite rare that algorithm can not only extract knowledge from the data, but also produce something beautiful using exactly the same set of training rules without any modifications.

    SOFM is a great example of the algorithm that can produce simple work of art when used in the right way.


    Tags: sofm, unsupervised, visualization, art
  • 2017 Dec 09 Self-Organizing Maps and Applications

    Self-Organizing Feature Map (SOFM or SOM) is a simple algorithm for unsupervised learning. It can be applied to solve vide variety of problems. It quite good at learning topological structure of the data and it can be used for visualizing deep neural networks.

    This article explains how SOFM works and shows different applications where it can be used.


    Tags: sofm, deep learning, image recognition, unsupervised, visualization, clustering
  • 2016 Dec 17 Hyperparameter optimization for Neural Networks

    This article explains different hyperparameter algorithms that can be used for neural networks. It covers simple algorithms like Grid Search, Random Search and more complicated algorithms like Gaussian Process and Tree-structured Parzen Estimators (TPE).


    Tags: visualization, backpropagation, supervised, hyperparameter optimization
  • 2015 Jul 04 Visualize Algorithms based on the backpropagation

    Typical neural networks have mullions of parameters and it's quite difficult to visualize the process. In the article, we visualize training of the network that has only 2 parameters. It allows us to explore different training algorithms and see how it behaves during the training

    These type of visualizations can provide us with useful insights about the training process.


    Tags: supervised, backpropagation, visualization

Search

Install NeuPy

pip install neupy

Learn more about NeuPy reading tutorials and documentation.

Issues and feature requests

If you find a bug or want to suggest a new feature feel free to create an issue on Github

© Copyright 2015 - 2019, Yurii Shevchuk. Powered by Tinkerer and Sphinx.