https://github.com/aaaastark/perceptron-neural-network

Artificial Natural Network Perceptron (Forward Pass and Back Propagation). Weights and Bias. Forward Pass: Net Input Function, Activation Function (Sigmoid). Threshold. Back Propagation: Binary Cross Entropy Loss, Computing Gradients/ Slopes/ Derivatives, Gradient Descent Step, Epoch.
https://github.com/aaaastark/perceptron-neural-network

artificial-neural-networks bias epoch gradient-descent perceptron python sigmoid threshold

Last synced: 25 days ago
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Artificial Natural Network Perceptron (Forward Pass and Back Propagation). Weights and Bias. Forward Pass: Net Input Function, Activation Function (Sigmoid). Threshold. Back Propagation: Binary Cross Entropy Loss, Computing Gradients/ Slopes/ Derivatives, Gradient Descent Step, Epoch.

• Host: GitHub
• URL: https://github.com/aaaastark/perceptron-neural-network
• Owner: aaaastark
• Created: 2022-05-11T21:36:47.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2022-05-11T21:45:39.000Z (about 4 years ago)
• Last Synced: 2025-10-09T16:06:04.889Z (8 months ago)
• Topics: artificial-neural-networks, bias, epoch, gradient-descent, perceptron, python, sigmoid, threshold
• Language: Jupyter Notebook
• Homepage: https://github.com/aaaastark/perceptron-neural-network
• Size: 60.5 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Perceptron Neural Network

## Artificial Natural Network Perceptron (Forward Pass and Back Propagation).

## Weights and Bias.

## Forward Pass: Net Input Function, Activation Function (Sigmoid).

## Threshold.

## Back Propagation: Binary Cross Entropy Loss, Computing Gradients/ Slopes/ Derivatives, Gradient Descent Step, Epoch.

#### Task Perceptron

1. Write code to perform N number of epochs until the loss gets close to zero.

3. Compute the loss after each epcoh using sklearn loss function.

5. Try different values of alpha and see how it affects the training process.

7. Once the perceptron gets trained, test the trained perceptron on testing data and report test accuracy, confusion matrix.