With this package, you can generate mixed-integer linear programming (MIP) models of trained artificial neural networks (ANNs) using the rectified linear unit (ReLU) activation function. At the moment, only TensorFlow sequential models are supported. Interfaces to either the Pyomo or Gurobi modeling environments are offered.

QReLU and m-QReLU: Two novel quantum activation functions for Deep Learning in TensorFlow, Keras, and PyTorch

layers

A Feed Forward Neural Network which a ReLU activation, Cross Entropy Loss & Adam Optimizer

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

유전알고리즘과 인공신경망을 활용허여 마리오 학습

Explain fully connected ReLU neural networks using rules

Python from-scratch implementation of a Neural Network Classifier. Dive into the fundamentals of approximation, non-linearity, regularization, gradients, and backpropagation.

This is a Feed-Forward Neural Network with back-propagation written in C++ from scratch with no external libraries.

Hackathon project. This project uses object detection to identify and classify waste detected in the image with the help of image detection in python and neural networks.

This repository helps in understanding vanishing gradient problem with visualization

"The 'Activation Functions' project repository contains implementations of various activation functions commonly used in neural networks. "

his project is about building a artificial neural network using pytorch library. I am sharing the code and output for my project. Though there are many libraries out there that can be used for deep learning i like the pytorch most. As a python programmer, one of the reasons behind my liking is pythonic behaviour of pytorch.

My extensive work on Multiclass Image classification based on Intel image classification dataset from Kaggle and Implemented using Pytorch 🔦

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

Image Compression using one hidden layer Neural Network

The repository consists of a recommendation engine that suggests movies to the users based on the genre and ratings previously received. Under the hood, a neural collaborative filtering technique has been implemented

The main aim of this project is to built a predictive model using G Store data to predict the TOTAL REVENUE per customer that helps in better use of marketing budget.

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

This project involves the use of classical neural networks for the computation of the heat equation with Neumann boundary conditions and a gaussian distribution as initial condition.

"A TensorFlow-based neural network model for classifying handwritten digits from the MNIST dataset."

NU Bootcamp Module 21

Good Seed were employed Data Science for alcohol law compliance. My role includes using specialized cameras at checkout for alcohol buys, applying advanced computer vision for age verification, and designing a model to confirm age. I built a model with ResNet50 and 'relu', using a single neuron to output.

Here, we will provide a PyTorch regime to handle the partial differential equation solution of the heat equation by executing Deep Kolmogorov Method of Beck et. al.

prediction of an absolute temperature on the surface of a star using neural networks

Experimenting different neural network architectures for detecting spam emails

Building Generative Adversarial Networks

Applying neural network with adam optimizer on heart failure clinical records dataset to compare test errors of sigmoid, tanh, and relu activation functions

American Sign Language (ASL) Detection using CNN

This project utilizes a CNN model to classify cat and dog images through training and testing processes. The model is created using the Keras library on the TensorFlow backend.