Framework that uses artificial intelligence applied to mathematical models to make predictions

Related tags

Deep LearningLiconIA
Overview

LiconIA

Framework that uses artificial intelligence applied to mathematical models to make predictions

GitHub Release GitHub license


Interface Overview

image

Table of contents

[TOC]


1 Articles, theses for technical support

1.1 Final Coursework



1.2 Dissertation



1.3 Theses


loading...


1.4 Articles



2 Requirements


  • Python 3.6 or superior (sudo apt-get install python3.6 under Linux)
  • Python virtual environment (sudo apt-get install python3.6-venv under Linux)

3 Package structure (need to update)


The initial directory structure should look like this:


4 Development and Tests

4.1 Installing the package


Start by creating a new virtual environment for your project. Next, update the packages pip and setuptools to the latest version. Then install the package itself.

$ sudo apt-get install python3-tk
$ /usr/bin/python3.6 -m venv --prompt="LiconIA" venv
$ source venv/bin/activate
(LiconIA) $ pip install --upgrade setuptools pip
(LiconIA) $ pip install numpy matplotlib pandas xlrd PyQt5
(LiconIA) $ pip install xlrd==1.2.0

4.2 Run program


To run the code just type:

$ python run.py

4.3 Qt-desing Information


Information about running the interface in qt-desing https://pythonbasics.org/qt-designer-python/

How to start Designer

$ cd /usr/lib/x86_64-linux-gnu/qt5/bin/ && ./designer
$ ./designer

Information about widgets the interface in qt-desing https://doc.qt.io/qtforpython/PySide2/QtWidgets/

Convert ui to py

pyuic5 /home/linux/helloworld.ui -o helloworld.py

4.4 Code checking


It is also possible to check for errors in Python code using:

use a pep8 -> pycodestyle


4.5 Development Team



5 License


This package is released and distributed under the license GNU GPL Version 3, 29 June 2007.

MultiLexNorm 2021 competition system from ÚFAL

ÚFAL at MultiLexNorm 2021: Improving Multilingual Lexical Normalization by Fine-tuning ByT5 David Samuel & Milan Straka Charles University Faculty of

ÚFAL 13 Jun 28, 2022
Code for the AI lab course 2021/2022 of the University of Verona

AI-Lab Code for the AI lab course 2021/2022 of the University of Verona Set-Up the environment for the curse Download Anaconda for your System. Instal

Davide Corsi 5 Oct 19, 2022
Plotting points that lie on the intersection of the given curves using gradient descent.

Plotting intersection of curves using gradient descent Webapp Link --- What's the app about Why this app Plotting functions and their intersection. A

Divakar Verma 2 Jan 09, 2022
Codebase for Image Classification Research, written in PyTorch.

pycls pycls is an image classification codebase, written in PyTorch. It was originally developed for the On Network Design Spaces for Visual Recogniti

Facebook Research 2k Jan 01, 2023
SphereFace: Deep Hypersphere Embedding for Face Recognition

SphereFace: Deep Hypersphere Embedding for Face Recognition By Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj and Le Song License SphereFa

Weiyang Liu 1.5k Dec 29, 2022
QKeras: a quantization deep learning library for Tensorflow Keras

QKeras github.com/google/qkeras QKeras 0.8 highlights: Automatic quantization using QKeras; Stochastic behavior (including stochastic rouding) is disa

Google 437 Jan 03, 2023
pq is a jq-like Pickle file viewer

pq PQ is a jq-like viewer/processing tool for pickle files. howto # pq '' file.pkl {'other': 456, 'test': 123} # pq 'table' file.pkl |other|test| | 45

3 Mar 15, 2022
A Python reference implementation of the CF data model

cfdm A Python reference implementation of the CF data model. References Compliance with FAIR principles Documentation https://ncas-cms.github.io/cfdm

NCAS CMS 25 Dec 13, 2022
Official repository of my book: "Deep Learning with PyTorch Step-by-Step: A Beginner's Guide"

This is the official repository of my book "Deep Learning with PyTorch Step-by-Step". Here you will find one Jupyter notebook for every chapter in the book.

Daniel Voigt Godoy 340 Jan 01, 2023
PFLD pytorch Implementation

PFLD-pytorch Implementation of PFLD A Practical Facial Landmark Detector by pytorch. 1. install requirements pip3 install -r requirements.txt 2. Datas

zhaozhichao 669 Jan 02, 2023
PyTorch-centric library for evaluating and enhancing the robustness of AI technologies

Responsible AI Toolbox A library that provides high-quality, PyTorch-centric tools for evaluating and enhancing both the robustness and the explainabi

24 Dec 22, 2022
Code for approximate graph reduction techniques for cardinality-based DSFM, from paper

SparseCard Code for approximate graph reduction techniques for cardinality-based DSFM, from paper "Approximate Decomposable Submodular Function Minimi

Nate Veldt 1 Nov 25, 2022
PyZebrascope - an open-source Python platform for brain-wide neural activity imaging in behaving zebrafish

PyZebrascope - an open-source Python platform for brain-wide neural activity imaging in behaving zebrafish

1 May 31, 2022
[CIKM 2021] Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning

Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning. This repo contains the PyTorch code and implementation for the paper E

Akuchi 18 Dec 22, 2022
Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition

Light-SERNet This is the Tensorflow 2.x implementation of our paper "Light-SERNet: A lightweight fully convolutional neural network for speech emotion

Arya Aftab 29 Nov 12, 2022
Code accompanying the paper Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs (Chen et al., CVPR 2020, Oral).

Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs This repository contains PyTorch implementation of our pa

Shizhe Chen 178 Dec 29, 2022
UMEC: Unified Model and Embedding Compression for Efficient Recommendation Systems

[ICLR 2021] "UMEC: Unified Model and Embedding Compression for Efficient Recommendation Systems" by Jiayi Shen, Haotao Wang*, Shupeng Gui*, Jianchao Tan, Zhangyang Wang, and Ji Liu

VITA 39 Dec 03, 2022
Calling Julia from Python - an experiment on data loading

Calling Julia from Python - an experiment on data loading See the slides. TLDR After reading Patrick's blog post, we decided to try to replace C++ wit

Abel Siqueira 8 Jun 07, 2022
Code for ICCV2021 paper PARE: Part Attention Regressor for 3D Human Body Estimation

PARE: Part Attention Regressor for 3D Human Body Estimation [ICCV 2021] PARE: Part Attention Regressor for 3D Human Body Estimation, Muhammed Kocabas,

Muhammed Kocabas 277 Jan 03, 2023