Dimag, Nepali for the brain is an object-oriented neural network framework developed by me using python3.
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Updated
Oct 21, 2021 - Python
Dimag, Nepali for the brain is an object-oriented neural network framework developed by me using python3.
Fundamental Machine Learning Algorithms implemented from scratch. This is an on going repository and will be updated in future.
A simple homebrew neural network created for MTE 203.
CNN model for MNIST dataset implemented from scratch using NumPy
Trained deep neural networks to predict and classify input image (MNISTDD) datasets with python.
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
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.
Implementation of a simple neural network in numpy.
Create a few popular Neural Networks from scratch using just Numpy
Small NeuralNet-Framework implemented with NumPy (Convolution|TransposeConv|Linear)
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 :-)
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.
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 :-)
TCC do curso de Análise e Desenvolvimento de Sistemas - FATEC - A Utilização de Algoritmos Genéticos na Otimização de Problemas
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