There are 2 repositories under relu-layer topic.
Implementing Neural Networks for Computer Vision in autonomous vehicles and robotics for classification, pattern recognition, control. Using Python, numpy, tensorflow. From basics to complex project
Sentiment analysis for Twitter's tweet (in Indonesia language) was built with 3 models to get a comparison in determining which model gives the best results for predicting a tweet to have a positive or negative meaning.
Simple MATLAB toolbox for deep learning network: Version 1.0.3
QReLU and m-QReLU: Two novel quantum activation functions for Deep Learning in TensorFlow and Keras
A facial emotion/expression recognition model created using CNN with Keras & Tensorflow
Super Resolution's the images by 3x using CNN
Convolutional Neural Network with just Numpy and no other MLLibs
Neural Network to predict which wearable is shown from the Fashion MNIST dataset using a single hidden layer
Corruption Robust Image Classification with a new Activation Function. Our proposed Activation Function is inspired by the Human Visual System and a classic signal processing fix for data corruption.
A classifier to differentiate between Cat and Non-Cat Images
Neural Network from scratch without any machine learning libraries
A small walk-through to show why ReLU is non linear!
The objective of this project is to identify the fraudulent transactions happening in E-Commerce industry using deep learning.
Project inspired by a book titled, "Artificial Intelligence," by Copeland. One of the World's first Quantum Neural Networks ever invented.
Using MNSIT as a training dataset, this model is trained to predict the handwritten digits.
Building Convolution Neural Networks from Scratch
Twitter Sentiment Extraction using Custom Roberta Transformer Model and using Pre-trained model weights for prediction
This project creates a machine learning model that predicts the success of investing in a business venture.
Backward pass of ReLU activation function for a neural network.
Our custom AI Pipeline on image classification for 2019 Chung-ang-University-hackathon.
Identifying text in images in different fonts using deep neural network techniques.
rede neural totalmente conectada, utilizando mini-batch gradient descent e softmax para classificação no dataset MNIST
Gesture Recognition by CNN created using Networks Library created by me.
Traffic signal identification using Keras LeNet architecture. Identify 43 different classes of images with over 90% accuracy.
Feed Forward Neural Network to classify the FB post likes in classes of low likes or moderate likes or high likes, back propagtion is implemented with decay learning rate method
Text Generation
Built MLP with ReLU and Adam optimization with 2 layers, 3 layers and 5 layers and observed how it works.
Simple DNN code, adapted from Nielsen
Channelwise Partial Convolutions for hardware aware applications
Sequential Convolutional Neural Network for handwritten digits recognition trained on MNIST dataset using keras API