Neural Network Framework is a from-scratch implementation of feedforward neural networks built entirely on NumPy. It gives you full control over every layer, activation function, weight initializer, and training loop — making it ideal for learning, research, and experimentation.Documentation Index
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Introduction
Understand what the framework provides and when to use it.
Quickstart
Build and train your first neural network in minutes.
Core Concepts
Learn about layers, activations, loss functions, and weight initialization.
Examples
Walk through XOR, autoencoder, and MNIST classification examples.
What You Can Build
Neural Network Framework supports a range of architectures and use cases:Classification
Multi-class classifiers with softmax output and cross-entropy loss.
Regression
Continuous output networks with linear activations and MSE loss.
Autoencoders
Encoder-decoder networks for dimensionality reduction and reconstruction.
Key Features
Define your layers
Create an
InputLayer, one or more HiddenLayers, and an OutputLayer. Each layer accepts a neuron count and activation function.Initialize weights and biases
Choose from
xavier, he, lecun, normal_random, or uniform_random initialization strategies per layer.Train with gradient descent
Use
gradient_descent_epoch for a fixed number of epochs, or gradient_descent_threshold to stop automatically when the loss drops below a target.Neural Network Framework requires only NumPy and Matplotlib. No TensorFlow, PyTorch, or other deep learning libraries needed for core functionality.