arturbeg Goto Github PK

analysis

angular-forms

https://angular.io/guide/forms

angular-reactive-forms

angular2-django-movies

Sample app pairing Angular 2 as frontend and Django as API

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

bubble

Speech bubble for Flutter

casino-ethereum

This is a simple casino build with solidity, truffle and react

cs224u

Code for Stanford CS224u

django-angular-jwt-seed

seed project for angular, django, jwt

django-rest-angular2-example

A simple example of a Django REST app + Angular2

django_realtime_reddit_clone_archive

A real-time reddit clone build just using Django (web-socket functionality is implemented via Django Channels). Archive repository migrated from BitBucket

dni.pytorch

drf-angular4-jwt

JWT based authentication on angular4 application using django-rest-framework For CRUD operation

drfa

econowcast

Experimental tools (R) for Big Data econometrics nowcasting and early estimates

efficient_transformer

Scaling Transformer architectures has been critical for pushing the frontiers of Language Modelling (LM), a problem central to Natural Language Processing (NLP) and Language Understanding. Although there is a direct positive relationship between the Transformer capacity and its LM performance, there are practical limitations which make training massive models impossible. These limitations come in the form of computation and memory costs which cannot be solely addressed by training on parallel devices. In this thesis, we investigate two approaches which can make Transformers more computationally and memory efficient. First, we introduce the Mixture-of-Experts (MoE) Transformer which can scale its capacity at a sub-linear computational cost. Second, we present a novel content-based sparse attention mechanism called Hierarchical Self Attention (HSA). We demonstrate that the MoE Transformer is capable of achieving lower test perplexity values than a vanilla Transformer model with higher computational demands. Language Modelling experiments, involving a Transformer which uses HSA in place of conventional attention, revealed that HSA can speed up attention computation by up to 330% at a negligible cost in model performance.

fast-transformers

Pytorch library for fast transformer implementations

flutter-chat-app

A chat app built on Flutter with firebase authentication and image sharing capability.

flutter-facebook-auth

Facebook authentication demo in Flutter

fluttergram

A working Instagram clone written in Flutter using Firebase / Firestore

flutterwhatsappclone

Building a WhatsApp Clone in Flutter.

fractal

fractal-angular

fractal-angular-prod

Fractal is an ML-powered network of interconnected public chats that allows branching of chats into more focused “sub-chats”, thereby overcoming the problem of rapid conversation subject dilution and low engagement. Fractal aims to allow unacquainted individuals to spontaneously find and discuss niche topics of common interest in real-time.

fractal-django-prod

fractal-node

fractal_flutter

Fractal is an ML-powered network of interconnected public chats that allows branching of chats into more focused “sub-chats”, thereby overcoming the problem of rapid conversation subject dilution and low engagement. Fractal aims to allow unacquainted individuals to spontaneously find and discuss niche topics of common interest in real-time.

google_trends_consumption_prediction

This work investigates the forecasting relationship between a Google Trends indicator and real private consumption expenditure in the US. The indicator is constructed by applying Kernel Principal Component Analysis to consumption-related Google Trends search categories. The predictive performance of the indicator is evaluated in relation to two conventional survey-based indicators: the Conference Board Consumer Confidence Index and the University of Michigan Consumer Sentiment Index. The findings suggest that in both in-sample and out-of-sample nowcasting estimations the Google indicator performs better than survey-based predictors. The results also demonstrate that the predictive performance of survey-augmented models is no different than the power of a baseline autoregressive model that includes macroeconomic variables as controls. The results demonstrate an enormous potential of Google Trends data as a tool of unmatched value to forecasters of private consumption.

hn_app

The HN reader app developed live on The Boring Flutter Development Show

mac-network

Implementation for the paper "Compositional Attention Networks for Machine Reasoning" (Hudson and Manning, ICLR 2018)