Contra is a lightweight, production ready Tensorflow alternative for solving time series prediction challenges with AI

Related tags

Deep Learningaitimeseriesalgorithmspredictionsprediction-algorithmprediction-modelaiprediction
Overview

GitHub contributors GitHub forks GitHub watchers GitHub stars GitHub issues MIT license


Logo

Contra AI Engine

A lightweight, production ready Tensorflow alternative developed by Styvio
styvio.com »

How to Use · Report Bug · Request Feature

Table of Contents
  1. About Contra
  2. Getting Started
  3. PyPi Module Page
  4. License
  5. Contact

About The Project

Welcome to Contra, a fully open source AI engine developed by Styvio. Contra is a lightweight, production ready alternative to Tensorflow. Contra is written fully in Python and built to solve complex problems with AI. Contra is built specifically to solve time series prediction problems, server or client side.

What is Contra?

Contra is a collections of functions that allow you to solve complex time series problems with AI. If you have used or built or used prediction algorithms before, you will fully understand what is going on under the hood here. Contra is based on the simple KNN algorithm, but with one major modification. Instead of averaging the closest data points, the prediction is based on a linear combination of the sum of all other previous outcomes, and their distance to the requested prediction in terms of their step function.

Why does Contra exist?

Other AI engines or libraries can sometimes take minutes to run, and produce weights files that are far to large to run directly on a production website. It is frusterating having to perform complex calculations off-server and then to sending the results over to a database via an API. It is equally frusterating having weights files that are over 0.5 GB large. These challenges make libraries like Tensorflow useless in solving complex problems fast. Using tensorflow for time series prediction is like attatching a V8 engine to a lawn mower. We have become far too farmiliar with these challenges, and built Contra to erase this headache.

How can I use Contra?

You can use Contra anywhere you need to solve a complex problem with AI. Contra can be applied anywhere annalysis of time series data using AI prediction makes sense. Contra comes with the MIT liscence, meaning you can use it for personal and commercial projects without worying about liscence fees, giving credit, or litigation.

Getting Started

This section provides assistance on how to install Contra, as well as a general rundown on using the module.

Installation

pip install Contra (coming soon)

Quickstart

Now that the Contra module is installed, it can be imported in any .py python file, wherever you normally write code.

You might also like...
FwordCTF 2021 Infrastructure and Source code of Web/Bash challenges

FwordCTF 2021 You can find here the source code of the challenges I wrote (Web and Bash) in FwordCTF 2021 and the source code of the platform with our

Kahla 5 Nov 25, 2022
Writeups for the challenges from DownUnderCTF 2021

cloud Challenge Author Difficulty Release Round Bad Bucket Blue Alder easy round 1 Not as Bad Bucket Blue Alder easy round 1 Lost n Found Blue Alder m

DownUnderCTF 161 Dec 31, 2022
CTF challenges and write-ups for MicroCTF 2021.

MicroCTF 2021 Qualifications About This repository contains CTF challenges and official write-ups for MicroCTF 2021 Qualifications. License Distribute

Shellmates 12 Dec 27, 2022
Code for all the Advent of Code'21 challenges mostly written in python

Advent of Code 21 Code for all the Advent of Code'21 challenges mostly written in python. They are not necessarily the best or fastest solutions but j

null 4 May 26, 2022
A python script to dump all the challenges locally of a CTFd-based Capture the Flag.
A python script to dump all the challenges locally of a CTFd-based Capture the Flag.

A python script to dump all the challenges locally of a CTFd-based Capture the Flag. Features Connects and logins to a remote CTFd instance. Dumps all

Podalirius 77 Dec 7, 2022
Price-Prediction-For-a-Dream-Home - A machine learning based linear regression trained model for house price prediction.
Price-Prediction-For-a-Dream-Home - A machine learning based linear regression trained model for house price prediction.

Price-Prediction-For-a-Dream-Home ROADMAP TO THIS LINEAR REGRESSION BASED HOUSE PRICE PREDICTION PREDICTION MODEL Import all the dependencies of the p

DIKSHA DESWAL 1 Dec 29, 2021
Doge-Prediction - Coding Club prediction ig

Doge-Prediction Coding Club prediction ig Basically: Create an application that

null 1 Jan 10, 2022
PyArmadillo: an alternative approach to linear algebra in Python

PyArmadillo is a linear algebra library for the Python language, with an emphasis on ease of use.

Terry Zhuo 58 Oct 11, 2022
A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection
A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection

Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection 1. 介绍 用以替代 NMS，在所有 bbox 中挑选出最优的集合。 NMS 仅考虑了 bbox 的得分，然后根据 IOU 来

null 44 Sep 15, 2022
Releases(PublicRelease)
Owner
styvio
styvio
GitHub Repository https://www.styvio.com/
Relative Uncertainty Learning for Facial Expression Recognition

Relative Uncertainty Learning for Facial Expression Recognition The official implementation of the following paper at NeurIPS2021: Title: Relative Unc

35 Dec 28, 2022
This repository for project that can Automate Number Plate Recognition (ANPR) in Morocco Licensed Vehicles. 💻 + 🚙 + 🇲🇦 = 🤖 🕵🏻‍♂️

MoroccoAI Data Challenge (Edition #001) This Reposotory is result of our work in the comepetiton organized by MoroccoAI in the context of the first Mo

SAFOINE EL KHABICH 14 Oct 31, 2022
In this project we investigate the performance of the SetCon model on realistic video footage. Therefore, we implemented the model in PyTorch and tested the model on two example videos.

Contrastive Learning of Object Representations Supervisor: Prof. Dr. Gemma Roig Institutions: Goethe University CVAI - Computational Vision & Artifici

Dirk Neuhäuser 6 Dec 08, 2022
A scikit-learn compatible neural network library that wraps PyTorch

A scikit-learn compatible neural network library that wraps PyTorch. Resources Documentation Source Code Examples To see more elaborate examples, look

4.9k Jan 03, 2023
Dataset VSD4K includes 6 popular categories: game, sport, dance, vlog, interview and city.

CaFM-pytorch ICCV ACCEPT Introduction of dataset VSD4K Our dataset VSD4K includes 6 popular categories: game, sport, dance, vlog, interview and city.

96 Jul 05, 2022
Code to compute permutation and drop-column importances in Python scikit-learn models

Feature importances for scikit-learn machine learning models By Terence Parr and Kerem Turgutlu. See Explained.ai for more stuff. The scikit-learn Ran

Terence Parr 537 Dec 31, 2022
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition

RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition (PyTorch) Paper: https://arxiv.org/abs/2105.01883 Citation: @

260 Jan 03, 2023
Companion repo of the UCC 2021 paper "Predictive Auto-scaling with OpenStack Monasca"

Predictive Auto-scaling with OpenStack Monasca Giacomo Lanciano*, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella 2021 IEEE/ACM 14t

Giacomo Lanciano 0 Dec 07, 2022
Gym environment for FLIPIT: The Game of "Stealthy Takeover"

gym-flipit Gym environment for FLIPIT: The Game of "Stealthy Takeover" invented by Marten van Dijk, Ari Juels, Alina Oprea, and Ronald L. Rivest. Desi

Lisa Oakley 2 Dec 15, 2021
Files for a tutorial to train SegNet for road scenes using the CamVid dataset

SegNet and Bayesian SegNet Tutorial This repository contains all the files for you to complete the 'Getting Started with SegNet' and the 'Bayesian Seg

Alex Kendall 800 Dec 31, 2022
HarDNeXt: Official HarDNeXt repository

HarDNeXt-Pytorch HarDNeXt: A Stage Receptive Field and Connectivity Aware Convolution Neural Network HarDNeXt-MSEG for Medical Image Segmentation in 0

5 May 26, 2022
A high-performance anchor-free YOLO. Exceeding yolov3~v5 with ONNX, TensorRT, NCNN, and Openvino supported.

YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. For more details, please refer to our rep

7.7k Jan 06, 2023
DeepFaceLive - Live Deep Fake in python, Real-time face swap for PC streaming or video calls

DeepFaceLive - Live Deep Fake in python, Real-time face swap for PC streaming or video calls

8.3k Dec 31, 2022
A Python package to process & model ChEMBL data.

insilico: A Python package to process & model ChEMBL data. ChEMBL is a manually curated chemical database of bioactive molecules with drug-like proper

Steven Newton 0 Dec 09, 2021
2nd solution of ICDAR 2021 Competition on Scientific Literature Parsing, Task B.

TableMASTER-mmocr Contents About The Project Method Description Dependency Getting Started Prerequisites Installation Usage Data preprocess Train Infe

Jianquan Ye 298 Dec 21, 2022
This repository contains PyTorch code for Robust Vision Transformers.

This repository contains PyTorch code for Robust Vision Transformers.

117 Dec 07, 2022
Towards Fine-Grained Reasoning for Fake News Detection

FinerFact This is the PyTorch implementation for the FinerFact model in the AAAI 2022 paper Towards Fine-Grained Reasoning for Fake News Detection (Ar

Ahren_Jin 15 Dec 15, 2022
PyTorch implementation for 3D human pose estimation

Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach This repository is the PyTorch implementation for the network presented in:

Xingyi Zhou 579 Dec 22, 2022
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy" (ICLR 2022 Spotlight)

About Code release for Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy (ICLR 2022 Spotlight)

THUML @ Tsinghua University 221 Dec 31, 2022
Code for IntraQ, PyTorch implementation of our paper under review

IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization paper Requirements Python = 3.7.10 Pytorch == 1.7

1 Nov 19, 2021