Python Flask-machine Resources

A DNN inference latency prediction toolkit for accurately modeling and predicting the latency on diverse edge devices.

Genalog is an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities.

Sum-Product Probabilistic Language SPPL is a probabilistic programming language that delivers exact solutions to a broad range of probabilistic infere

Contrastive Learning for Many-to-many Multilingual Neural Machine Translation(mCOLT/mRASP2), ACL2021 The code for training mCOLT/mRASP2, a multilingua

This project is made to help you scale from a basic Machine Learning project for research purposes to a production grade Machine Learning web service

This handbook accompanies the course: Machine Learning with Hung-Yi Lee

PyGCL is an open-source library for graph contrastive learning (GCL), which features modularized GCL components from published papers, standardized evaluation, and experiment management.

random-person-api API que entrega dados aleatórios sobre pessoas que (provavelmente) não existem. Como usar? Copie o link abaixo https://random-person

ThinkTwice ThinkTwice is a retriever-reader architecture for solving long-text machine reading comprehension. It is based on the paper: ThinkTwice: A

SG-NLP Machine learning models from Singapore's natural language processing (NLP) research community. sgnlp is a Python package that allows you to eas

Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber

ml4h is a toolkit for machine learning on clinical data of all kinds including genetics, labs, imaging, clinical notes, and more

H3DS Dataset This repository contains the code for using the H3DS dataset introduced in H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction Access

Leetcode compensations report Scraping and analysis of leetcode-compensations page.

PyGCL: Graph Contrastive Learning for PyTorch PyGCL is an open-source library for graph contrastive learning (GCL), which features modularized GCL com

Convert images of LaTex math equations into LaTex code.

An Introduction to Deep Learning for the Physical Layer An usable PyTorch implementation of the noisy autoencoder infrastructure in the paper "An Intr

Deep CORAL A PyTorch implementation of 'Deep CORAL: Correlation Alignment for Deep Domain Adaptation. B Sun, K Saenko, ECCV 2016' Deep CORAL can learn

xeasy-ml 1. What is xeasy-ml Xeasy-ml is a packaged machine learning framework. It allows a beginner to quickly build a machine learning model and use

背景 安装教程 快速上手 （一）预训练模型 （二）机器翻译 （三）文本分类 TenTrans 进阶 1. 多语言机器翻译 2. 跨语言预训练 背景 TrenTrans是一个统一的端到端的多语言多任务预训练平台，支持多种预训练方式，以及序列生成和自然语言理解任务。 安装教程 git clone git

Evidently helps analyze machine learning models during validation or production monitoring. The tool generates interactive visual reports and JSON profiles from pandas DataFrame or csv files. Currently 6 reports are available.

🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo

Brandnew-flask is still in the initial stage and needs to be updated and improved continuously. Everyone is welcome to maintain and improve this CLI.

Interpretable Machine Learning with Python, published by Packt

Flask Boilerplate to quickly get started with production grade flask application with some additional packages and configuration prebuilt.

Dynamic Routing Between Capsules - PyTorch implementation PyTorch implementation of NIPS 2017 paper Dynamic Routing Between Capsules from Sara Sabour,

Visual-Interaction-Networks An implementation of Deepmind visual interaction networks in Pytorch. Introduction For the purpose of understanding the ch

NIMA: Neural IMage Assessment This is a PyTorch implementation of the paper NIMA: Neural IMage Assessment (accepted at IEEE Transactions on Image Proc

PyTorch Dilated Recurrent Neural Networks PyTorch implementation of Dilated Recurrent Neural Networks (DilatedRNN). Getting Started Installation: $ pi

PyTorch NIMA: Neural IMage Assessment PyTorch implementation of Neural IMage Assessment by Hossein Talebi and Peyman Milanfar. You can learn more from

Non-Autoregressive Transformer Code release for Non-Autoregressive Neural Machine Translation by Jiatao Gu, James Bradbury, Caiming Xiong, Victor O.K.

Deepvoice3_pytorch PyTorch implementation of convolutional networks-based text-to-speech synthesis models: arXiv:1710.07654: Deep Voice 3: Scaling Tex

The revolutionary IDE for all NLP (Natural language processing) stuffs on the internet.

TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform

Pandas Machine Learning and Quant Finance Library Collection

Jinja Partials Simple reuse of partial HTML page templates in the Jinja template language for Python web frameworks. (There is also a Pyramid/Chameleo

⭐ [NEW] ⭐ T2F - 2.0 Teaser (coming soon ...) Please note that all the faces in the above samples are generated ones. The T2F 2.0 will be using MSG-GAN

ReSSL: Relational Self-Supervised Learning with Weak Augmentation This repository contains PyTorch evaluation code, training code and pretrained model

CobraML: Completely Customizable What is it? CobraML is a python library built on both numpy and numba. Unlike other ML libraries CobraML gives the us

MUSE stands for Multilingual Universal Sentence Encoder - multilingual extension (supports 16 languages) of Universal Sentence Encoder (USE).

IDRLnet IDRLnet is a machine learning library on top of PyTorch. Use IDRLnet if you need a machine learning library that solves both forward and inver

Voice2Series-Reprogramming Voice2Series: Reprogramming Acoustic Models for Time Series Classification International Conference on Machine Learning (IC

Towards Understanding and Mitigating Social Biases in Language Models This repo contains code and data for evaluating and mitigating bias from generat

This repository is a toolkit to do machine learning for programming languages. It implements tokenization, dataset preprocessing, model training and m

neraug This python library helps you with augmenting text data for named entity recognition. Augmentation Example Reference from An Analysis of Simple

AlphaFold This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP

This library was developed in order to facilitate rapid prototyping in Python of predictive machine-learning models using longitudinal medical data from an OMOP CDM-standard database.

NeuralCompression is a Python repository dedicated to research of neural networks that compress data. The repository includes tools such as JAX-based entropy coders, image compression models, video compression models, and metrics for image and video evaluation.

A simple tool for explore your object detection dataset. The goal of this library is to provide simple and intuitive visualizations from your dataset and automatically find the best parameters for generating a specific grid of anchors that can fit you data characteristics

Transparency-by-Design networks (TbD-nets) This repository contains code for replicating the experiments and visualizations from the paper Transparenc

This is a fork of Fairseq(-py) with implementations of the following models: Pervasive Attention - 2D Convolutional Neural Networks for Sequence-to-Se

This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices

deep-dream-in-pytorch Pytorch (https://github.com/pytorch/pytorch) implementation of the deep dream (https://en.wikipedia.org/wiki/DeepDream) computer

PyTorch Large-Scale Language Model A Large-Scale PyTorch Language Model trained on the 1-Billion Word (LM1B) / (GBW) dataset Latest Results 39.98 Perp

Overview PyTorch 0.4.1 | Python 3.6.5 Annotated implementations with comparative introductions for minimax, non-saturating, wasserstein, wasserstein g

Fully Adversarial Mosaics (FAMOS) Pytorch implementation of the paper "Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Imag

Graph Wavelet Neural Network ⠀⠀ A PyTorch implementation of Graph Wavelet Neural Network (ICLR 2019). Abstract We present graph wavelet neural network

Attention Walk ⠀⠀ A PyTorch Implementation of Watch Your Step: Learning Node Embeddings via Graph Attention (NIPS 2018). Abstract Graph embedding meth

SGCN ⠀ A PyTorch implementation of Signed Graph Convolutional Network (ICDM 2018). Abstract Due to the fact much of today's data can be represented as

Scalable Incomplete Network Embedding ⠀⠀ A PyTorch implementation of Scalable Incomplete Network Embedding (ICDM 2018). Abstract Attributed network em

GAM ⠀⠀ A PyTorch implementation of Graph Classification Using Structural Attention (KDD 2018). Abstract Graph classification is a problem with practic

SimGNN ⠀⠀⠀ A PyTorch implementation of SimGNN: A Neural Network Approach to Fast Graph Similarity Computation (WSDM 2019). Abstract Graph similarity s

Character Based CNN This repo contains a PyTorch implementation of a character-level convolutional neural network for text classification. The model a

APPNP ⠀ A PyTorch implementation of Predict then Propagate: Graph Neural Networks meet Personalized PageRank (ICLR 2019). Abstract Neural message pass

MixHop and N-GCN ⠀ A PyTorch implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019)

Splitter ⠀⠀ A PyTorch implementation of Splitter: Learning Node Representations that Capture Multiple Social Contexts (WWW 2019). Abstract Recent inte

CapsGNN ⠀⠀ A PyTorch implementation of Capsule Graph Neural Network (ICLR 2019). Abstract The high-quality node embeddings learned from the Graph Neur

Clockwork VAEs in JAX/Flax Implementation of experiments in the paper Clockwork Variational Autoencoders (project website) using JAX and Flax, ported

Newsemble is an API that provides easy access to the current news for programmatic analysis. It has been built using Python, BeautifulSoup and MongoDB.

SEAL ⠀⠀⠀ A PyTorch implementation of Semi-Supervised Graph Classification: A Hierarchical Graph Perspective (WWW 2019) Abstract Node classification an

PIX PIX is an image processing library in JAX, for JAX. Overview JAX is a library resulting from the union of Autograd and XLA for high-performance ma

Library | Paper | Slack We released two versions of OAG-BERT in CogDL package. OAG-BERT is a heterogeneous entity-augmented academic language model wh

Using python and scikit-learn to make stock predictions

Automated machine learning: Review of the state-of-the-art and opportunities for healthcare

GLAT Implementation for the ACL2021 paper "Glancing Transformer for Non-Autoregressive Neural Machine Translation" Requirements Python = 3.7 Pytorch

​ DS2.ai is an integrated AI operation solution that supports all stages from custom AI development to deployment. It is an AI-specialized platform service that collects data, builds a training dataset through data labeling, and enables automatic development of artificial intelligence and easy deployment and operation.

Lags valorant servers by rapidly picking up and throwing shorties.

Flask Boilerplate to quickly get started with production grade flask application with some additional packages and configuration prebuilt.

A collection of research papers and software related to explainability in graph machine learning.

A collection of Deep Learning images and visualizations. The project has been developed by the AI Summer team and currently contains almost 100 images.

A collection of Deep Learning images and visualizations. The project has been developed by the AI Summer team and currently contains almost 100 images.

EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis This repo contains the official implementations of EigenDamage: Structured Prunin

PyKEEN PyKEEN (Python KnowlEdge EmbeddiNgs) is a Python package designed to train and evaluate knowledge graph embedding models (incorporating multi-m

Generative Models Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine. Note: Gen

Deep Learning Winter School, November 2107. Tel Aviv Deep Learning Bootcamp : http://deep-ml.com. About Tel-Aviv Deep Learning Bootcamp is an intensiv

Torchreid Torchreid is a library for deep-learning person re-identification, written in PyTorch. It features: multi-GPU training support both image- a

Master Release Pytorch - Py + Nim A Nim frontend for pytorch, aiming to be mostly auto-generated and internally using ATen. Because Nim compiles to C+

ocaml-torch provides some ocaml bindings for the PyTorch tensor library. This brings to OCaml NumPy-like tensor computations with GPU acceleration and tape-based automatic differentiation.

tch-rs Rust bindings for the C++ api of PyTorch. The goal of the tch crate is to provide some thin wrappers around the C++ PyTorch api (a.k.a. libtorc

All-in-one web-based development environment for machine learning Getting Started • Features & Screenshots • Support • Report a Bug • FAQ • Known Issu

tymon An AI Assistant More Than a Toolkit The reason for creating framework tymon is simple. making AI more like an assistant, helping us to complete

Gesture Controlled Video Game Detailed Blog : https://www.analyticsvidhya.com/blog/2021/06/gesture-controlled-video-game/ Introduction This project is

XGBoost-Ray is a distributed backend for XGBoost, built on top of distributed computing framework Ray.

A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.

**Codebase and data are uploaded in progress. ** VOLT(-py) is a vocabulary learning codebase that allows researchers and developers to automaticaly ge

Knodle (Knowledge-supervised Deep Learning Framework) - a new framework for weak supervision with neural networks. It provides a modularization for se

spacy-huggingface-hub: Push your spaCy pipelines to the Hugging Face Hub This package provides a CLI command for uploading any trained spaCy pipeline

A small repository of projects built in my course, REST APIs with Flask and Python.

MooGBT is a library for Multi-objective optimization in Gradient Boosted Trees. MooGBT optimizes for multiple objectives by defining constraints on sub-objective(s) along with a primary objective. The constraints are defined as upper bounds on sub-objective loss function. MooGBT uses a Augmented Lagrangian(AL) based constrained optimization framework with Gradient Boosted Trees, to optimize for multiple objectives.

Crypto-Currency-Predictor This machine-learning algorithm takes in data from the last 60 days and tries to predict tomorrow's price of any crypto you