Tutorial detailing how to build a multilayer perceptron from scratch

MNIST MLP — Pure-C Neural Network from Scratch

A Matlab Implementation of Fully Connected Neural Network (MLP) from Scratch

Neural networks from scratch in NumPy, than using PyTorch, progressing from a single logistic neuron to a 2-layer MLP for binary classification and finally an MNIST digit classifier. Each notebook comes with a PDF explaining the math and the intuition behind the code,

Extensible neural network (MLP) built from scratch for didactic purpose

Pure JavaScript implementation of a Multi layer Perceptron (MLP) neural network from scratch in js using basic math, forward/backpropagation, and gradient descent. No ML libraries. Built for learning and experimentation.

Artificial Neural Network (MLP) and Deep Q-Learning Implementation from scratch, only using numpy.

An implementation of a Multilayer Perceptron (MLP) neural network from scratch to recognize handwritten digits.

Modular neural network framework written from scratch in C - specifically a Multi-Layer Perceptron (MLP)

Building a KAN neural network from scratch using Torch and TensorFlow. Comparing it to MLP built from Torch.

A CPU-based image classifier neural network framework implemented from scratch in Rust supporting basic MLPs and CNNs.

This project implements a multilayer perceptron (MLP) neural network in Rust from scratch. It includes forward and backward propagation for different modules such as `Linear`, `Sigmoid`, and `CrossEntropyLoss`.

We implemented a Multi-Layer Perceptron (MLP) model from scratch and compared its performance based on image classification accuracy on the "Fashion-MNIST" dataset to the performance of the Tensorflow Keras library's Convolutional Neural Network (CNN).

This project implements a feedforward neural network (Multilayer Perceptron) in Python for regression. It minimizes RMSE through training and validation over epochs, with trend visualization. Forward propagation, backpropagation, and weight updates are built entirely from scratch.

A simple pipeline of training neural networks in Python to classify Iris flowers from petal and sepal dimensions (can also be used on any other multiclass classification dataset). Implemented two neural network architectures along with the code to load data, train and optimize these networks.

This repository is a clean, educational reimplementation of Andrej Karpathy's micrograd project. It includes: - A minimal autograd engine (`Value` class) built from scratch - Neuron, Layer, and MLP abstractions built directly on top of `Value` - Manual training loop using scalar-based backpropagation along with Visualization

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mlp or Multi layer perceptron neural network from scratch in python

MLP Neural Network for recognising digits from an image made fully from scratch.

MNIST Digit classification using MLP (Multi Layer Perceptron) Neural Network in R from Scratch

An implementation of a Multi-Layer Perceptron (MLP) neural network from scratch using pure C++.

Multilayer perceptron (MLP) implemented in Python to recognize letters from images, built entirely from scratch without external neural network frameworks.

A simple multilayer perceptron (MLP), also known as a fully connected feedforward artificial neural network, written from scratch in Julia.

An implementation of a Multi-Layer Perceptron (MLP) neural network from scratch using NumPy for handwritten digit classification on the MNIST dataset

Built a fully functional multi-layer neural network (MLP) from scratch using only Numpy, manually implementing forward propagation, backpropagation, gradient descent, and weight initialization.

Implementation of a Multi-Layer Perceptron (MLP) from scratch to learn the fundamentals of neural networks. Features manual forward propagation, backpropagation, and gradient descent.

Educational Rust CLI for building, training, and visualizing simple neural networks (SLP & MLP) from scratch. Includes synthetic data generation, normalization, model training, prediction, and training history visualization. Ideal for learning Rust and neural networks.

Assignments for the "Neural Networks and Deep Learning" Course Faculty of Engineering, AUTh School of Informatics: Neural Network Architectures and Hybrid Models with Custom Implementations (SVM, MLP, and RBF NNs from scratch, CNNs, Autoencoders)

Mini projects done for the EEE443 Neural Networks course. Includes from scratch implementations (using Numpy) of LSTM, GRU, simple RNN, a MLP structure with SGD + Momentum and more.

🧠 Foundational Deep Learning Projects built from scratch using Python & NumPy. Includes Linear & Logistic Regression, Neural Networks, and MLP classification with visualizations. Ideal for beginners and ML enthusiasts!