neuralnetworknumpy 0.1.1

A neural network framework built completely from scratch using NumPy

NeuralNetworkFromScratch

A lightweight Python library implementing a fully functional neural network from scratch using NumPy, without relying on machine learning frameworks such as TensorFlow or PyTorch.

The goal of this project is to provide a clear and educational implementation of neural networks, including forward propagation, backpropagation, normalization, and regularization techniques.

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Features

• Fully connected neural network implementation
• Modular layer system
• Forward and backward propagation
• Batch normalization
• Dropout regularization
• ReLU and Softmax activation functions
• Dataset scaling utilities
• Train / validation split helpers

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Installation

Install from PyPI:

pip install neuralnetwork-from-scratch

Or install from source:

git clone https://github.com/Sendy45/NeuralNetworkFromScratch.git
cd NeuralNetworkFromScratch
pip install .

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Example Usage

import numpy as np
from keras.datasets import mnist

from NeuralNetworkFromScratch import (
    NeuralNetwork,
    Dense,
    ReLu,
    BatchNorm,
    Dropout,
    Softmax
)

# load dataset
(X_train, y_train), _ = mnist.load_data()

# flatten images
X_train = X_train.reshape(-1, 784) / 255.0

model = NeuralNetwork([
    Dense(64, inputs=784),
    ReLu(),
    BatchNorm(),
    Dropout(0.1),
    Dense(10),
    Softmax()
])

model.compile(
    optimizer="adam",
    loss="categorical_crossentropy"
)

model.fit(X_train, y_train, epochs=10, batch_size=32)

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Project Structure

NeuralNetworkFromScratch
│
├── neuralnet
│   ├── __init__.py
│   ├── network.py
│   ├── layers.py
│   ├── activations.py
│   └── utils.py
│
├── tests
├── README.md
└── pyproject.toml

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Goals of the Project

This project was designed to:

• Demonstrate how neural networks work internally
• Provide a clean NumPy-based implementation
• Serve as an educational resource for learning deep learning fundamentals

Unlike production ML frameworks, this project prioritizes clarity and learning over performance.

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Dependencies

• numpy
• tqdm

Optional dependencies used in examples:

• matplotlib
• keras (for datasets such as MNIST)

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License

This project is licensed under the MIT License.

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Author

Created by Itamar Senderovitz.