Visualizations of various activation functions for neural networks in TensorFlow

Neurona artificial

Kernel-Based Activation Functions implementation and experiments

Collection of my notes from Udacity's Intro to Deep Learning--> Introduction to Neural Networks course.

The objective of this repository is to provide a learning and experimentation environment to better understand the details and fundamental concepts of neural networks by building neural networks from scratch.

Implementation of a Fully Connected Neural Network, Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) from Scratch, using NumPy.

Artificial Neural Networks Activation Functions

Growing collection of different machine learning metrics.

to maintain activation functions used in machine learning

A NumPy based Neural Network Package Implementation

Robustness of Deep Neural Networks using Trainable Activation Functions

A Javascript version of Alexander Schiendorfer's blog post "A worked example of backpropagation".

The nonprofit foundation Alphabet Soup wants a tool that can help it select the applicants for funding with the best chance of success in their ventures. With your knowledge of machine learning and neural networks, you’ll use the features in the provided dataset to create a binary classifier that can predict whether applicants will be successful…

A set of experiments on Deep Neural Networks activation functions

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.

TorchAct, collection of activation function for PyTorch. https://pypi.org/project/torchact/

BSc Thesis at FER-2019/20 led by doc. dr. sc. Marko Čupić