Francesco Crecchi's repositories
Multiscale-Parametric-t-SNE
ESANN20 paper code repository. This package is a perplexity-free extension of Parametric t-SNE dimensionality reduction method implemented in `Keras` and compatible with `Scikit-learn`.
DropIn-ESN
This repository contains the code used to produce the results presented in the IJCNN 2017 paper "DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout" by D. Bacciu, F. Crecchi (University of Pisa) and D. Morelli (Biobeats LTD).
Intro_Keras
Introduction to Keras deep learning framework.
Classpad
Classpad
cleverhans
An adversarial example library for constructing attacks, building defenses, and benchmarking both
deepdrawing
This is the released code for our IEEE VIS19 Paper "DeepDrawing: A Deep Learning Approach to Graph Drawing".
DtNR
t-SNE based Deep Neural Rejection (DNR) repository.
FSharp-Interfaces-Apps
Some codes developed learning programming user interfaces in fsharp.
Game-Of-Life
Design Pattern exercise
GoL_optimized
SPM GoL final project
packer-windows
Windows Templates for Packer: Win10, Server 2016, 1709, 1803, 1809, 2019, 1903, 1909, 2004, Insider with Docker
Parametric-t-SNE-in-Keras
a python implementation of Parametric t-SNE in Keras
tensorflow
Computation using data flow graphs for scalable machine learning
Tensorflow-EchoStateNetwork
Echo state network implementation on tensorflow