https://github.com/subham-agrawall/neural-network-scratch
This repository has multi-layer perceptron (MLP) implemented from scratch to understand and grasp basic concepts.
https://github.com/subham-agrawall/neural-network-scratch
classification-models logistic-regression multilayer-perceptron-network neural-networks
Last synced: 7 months ago
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This repository has multi-layer perceptron (MLP) implemented from scratch to understand and grasp basic concepts.
- Host: GitHub
- URL: https://github.com/subham-agrawall/neural-network-scratch
- Owner: subham-agrawall
- Created: 2019-12-21T07:22:14.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2021-04-11T08:11:47.000Z (about 5 years ago)
- Last Synced: 2025-01-19T07:24:32.137Z (over 1 year ago)
- Topics: classification-models, logistic-regression, multilayer-perceptron-network, neural-networks
- Language: Python
- Homepage:
- Size: 532 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Multilayer Perceptron (MLP)
Implementation of multilayer perceptron/neural network from scratch to understand and grasp basic concepts of Deep Learning.
Requirements:
1. numpy
2. matplotlib
3. scikit-learn
Output plots:
1. Generated dataset for classification
2. Decision boundary from Logistic Regression model
3. Decision boundary from MLP with hidden layer size = 3
4. Variation in decision boundary with hidden layer size
This exercise shows the capability of neural network to form non-linear decision boundaries where logistic regression fails through an example. Also, shows the variation in decision boundary as we increase the number of nodes in hidden layer.