A simple homebrew neural network created for MTE 203.

Fundamental Machine Learning Algorithms implemented from scratch. This is an on going repository and will be updated in future.

Dimag, Nepali for the brain is an object-oriented neural network framework developed by me using python3.

Trained deep neural networks to predict and classify input image (MNISTDD) datasets with python.

CNN model for MNIST dataset implemented from scratch using NumPy

Neural Networks and training algorithms in Numpy, for learning purpose.

Neural networks

This is to see how a kernel will convolve over an image and what will be its output after convolution

NumPy-based feed-forward neural network

Implementation of a simple neural network in numpy.

Create a few popular Neural Networks from scratch using just Numpy

NumPy (short for Numerical Python) is a powerful Python library used for working with arrays, matrices, and numerical computations.

I made fully connected neural network in plain NumPy to classify digits from the MNIST dataset! It achieves 95% accuracy :-)

Small NeuralNet-Framework implemented with NumPy (Convolution|TransposeConv|Linear)

A proof of concept of a recursion doing stochastic gradient descent for a simple neural network. Done in Python3 with numpy

Classifies different types of wheat seeds by Artificial Neural Network using Numpy.

Assignment codes, and general work for pes io course Neural Networks Unleashed

I made LeNet5 (one of the first convolution neural networks) in plain NumPy to classify digits from the MNIST dataset! Accuracy reaches 91.5% after one epoch :-)

This repository contains an implementation of a neural network from scratch using only NumPy, a fundamental library for numerical computing in Python. The neural network is designed to perform tasks such as classification, regression, or any other supervised learning problem.