Implementing Artificial Neural Network training process in Python

This program implements logistic regression from scratch using the gradient descent algorithm in Python to predict whether customers will purchase a new car based on their age and salary.

Neural Network from scratch without any machine learning libraries

implementation of neural network from scratch only using numpy (Conv, Fc, Maxpool, optimizers and activation functions)

A simple neural network that defines functions for initializing a neural network, forward and backward propagation, and training. It uses a simple neural network architecture with sigmoid activation and binary cross-entropy loss for binary classification. The goal is to train the network to make accurate predictions for binary classification tasks.

Classification model using sigmoid activation with unknown class data

a multilayer neural net written in go

Deep-Learning neural network to analyze and classify the success of charitable donations.

Image Classification, one of the techniques belonging to object recognition can be used in analysing objects appearing in an image.

Predicting if a patient is diabetic or not by training a classification model using both Logistic regression and Artificial neural networks

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

Фреймворк глубоко обучения на Numpy, написанный с целью изучения того, как все работает под "капотом".

Simple DNN code, adapted from Nielsen

Minimal, limited in features, deep learning library, created with the goal of understanding more of the field.

Deep Forward Architecture From Scratch

Various applications of deep learning have been demonstrated.

It is small Web app for Visualization of Activation Function

This repository contains a basic implementation of a feed forward neural network using TensorFlow and Keras to predict the onset of diabetes in Pima Indian women based on certain diagnostic measures. The dataset used for training and evaluation is the Pima Indians Diabetes Database, which is publicly available and widely used for machine learning

A simple neural network with backpropagation used to recognize ASCII coded characters

Simple densely connected neural network with sigmoid activation.