Implementation of CORDIC Algorithms Using Verilog

Deep Learning

Simple multi layer perceptron application using feed forward back propagation algorithm

A data classification using MLP

Classes Angle, GeoPos, UTM32 and some other Math functions

Faster Java implementations of hypot, expm1, cos, sinh, cosh, tanh, asin, acos, atan and atan2

A neural network (NN) having two hidden layers is implemented, besides the input and output layers. The code gives choise to the user to use sigmoid, tanh orrelu as the activation function. Prediction accuracy is computed at the end.

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

Implementation of an ANN for recognisement of the Iris plant-family

Neural Network from scratch without any machine learning libraries

Generic L-layer 'straight in Python' fully connected Neural Network implementation using numpy.

GAAF implementation on Keras

Artificial Neural Networks Activation Functions

Modifies a neural network's hyperparameters, activation functions, cost functions, and regularization methods to improve training performance and generalization.

No description provided.

Lightweight neural network library written in ANSI-C supporting prediction and backpropagation for Convolutional- and Fully Connected neural networks

Compute the hyperbolic tangent of a number.

Neural network with 2 hidden layers

Create an iterator which evaluates the hyperbolic tangent for each iterated value.

Compute the hyperbolic cotangent of a number.

Time series forecast using RNN and LSTM

I have implemented some AI projects from scratch implementation without explicit use of the built-in-libraries and thus added to this repo.

Neural Network implementation from scratch along with its analysis with different type of activation function and with variation in hidden layer size and depth.

Predicting Song Popularity Using Neural Networks with Backpropagation Algorithm Based on Audio Features

No description provided.

Deep Learning model for predicting success after donation coded in Google Colab

This repository delves into the role of activation functions in perceptron-based classification models. It features a comprehensive Jupyter notebook demonstrating different activation functions, their mathematical foundations, and their impact on model performance.

2nd Project of Course 'Machine Learning' of the SMARTNET programme. Taken at the National and Kapodistrian University of Athens.

Advance Machine Learning (CSL 712) Course Lab Assignments

Comparison of common activation functions on MNIST dataset using PyTorch.