https://github.com/KirillShmilovich/MLP-Neural-Network-From-Scratch

Tutorial detailing how to build a multilayer perceptron from scratch
https://github.com/KirillShmilovich/MLP-Neural-Network-From-Scratch

Last synced: about 2 months ago
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Tutorial detailing how to build a multilayer perceptron from scratch

• Host: GitHub
• URL: https://github.com/KirillShmilovich/MLP-Neural-Network-From-Scratch
• Owner: KirillShmilovich
• Created: 2019-07-15T23:24:00.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-01-24T16:41:33.000Z (over 4 years ago)
• Last Synced: 2024-07-08T05:34:17.545Z (3 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 15 MB
• Stars: 62
• Watchers: 1
• Forks: 16
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# Building a multilayer perceptron from scratch

The mathematics and computation that drive neural networks are frequently seen as erudite and impenetrable. A clearly illustrated example of building from scratch a neural network for handwriting recognition is presented in `MLP.ipynb`. This tutorial provides a step-by-step overview of the mathematics and code used in many modern machine learning algorithms.

## Installation 

To view this notebook in your browser simply click the `MLP.ipynb` file above.

To run this notebook locally make sure you have [git](https://help.github.com/en/articles/set-up-git), [python](https://www.python.org/downloads/), and [Jupyter](https://jupyter.org/install) installed. 

Then in a terminal window:

```bash

$ git clone https://github.com/KirillShmilovich/MLP-Neural-Network-From-Scrath

$ cd MLP-Neural-Network-From-Scrath

$ jupyter-notebook MLP.ipynb

```