Author: Deep Learner
Simple Python project using Opencv and datetime package to recognise faces and log attendance data in a csv file
Simple Python project using Opencv and datetime package to recognise faces and log attendance data in a csv file.In “face_fetching.py” file I’ve automated the process of creating a custom dataset in an already existing folder. Each subfolder in the the “FaceData” folder has images of corresponding to the folder name e.g Samuel folder contains images of samuel.The “Face_sample_train.py” file trains a model to recognize the faces in the dataset.In the “Face_sample_train.py” file make sure you change the image directory accordingly […]
Read more
Project developed as part of a selection process for the company Denox
📝 Tabela de conteúdos 🧐 Sobre Projeto desenvolvido como parte de um processo seletivo da empresa Denox. Nesse projeto foi desenvolvido uma API usando Tornado onde tem a função de calcular a distância percorrida, o tempo em movimento, o tempo parado e os centroides das posições paradas de um veiculo rastreado. 📝 Requisitos para rodar o projeto Python3 Docker Docker Compose 💭 Instalação 1.Instale o Docker seguindo o tutorial a seguir:https://docs.docker.com/engine/install/ubuntu/
Read more
Search all history of Chrome in terminal
Search all history of Chrome in terminal. Demo Usages Move the Chrome history file to current directory by running move_history.sh Rename history_db_{timestamp} file to the name DB [Optional] migration.py script is to merge the newest history file to DB file ./migration.py history_db_1638637824, it will merge history_db_1638637824 to DB file chrotry to search title, url in terminal Shortcuts CTRL + n, move one line down CTRL + p, move one line up CTRL + e, quit GitHub View Github
Read moreA project designed to make taking notes easier than ever – by doing it all on command line
A project designed to make taking notes easier than ever – by doing it all on command line! Yes, all of your files are easily accessible through one command interface, and can be written to at any time! #ad #sponsored Honestly, I’m just making this as a curiosity for when I go back to school in the spring.My goal is to minimize effort and maximize note efficiency, making all of my notes easily accessible and well organized.Each entry you make […]
Read moreCommand-line tool to use LNURL with your LND instance
Commandline tool to use LNURL payRequest and withdrawRequest with LND. Usage: Customize config lndlnurl.conf Run python main.py Docker: docker run -t -i –rm -v cred:/code/cred:ro -v $PWD/lndlnurl.conf:/code/lndlnurl.conf ghcr.io/dsbaars/lnd-lnurl LNURL1DP68GURN8GHJ7MRWW3UXYMM59E3K7MF0D3H82UNV9ACXZ7FLW4EK2UNFVS7NXWPKXSURYYAF0CA Docker (on Umbrel machines): docker run -t -i –rm –network=”umbrel_main_network” -v ~/umbrel/lnd:/.lnd:ro -v $PWD/lndlnurl.conf:/code/lndlnurl.conf ghcr.io/dsbaars/lnd-lnurl:latest LNURL1DP68GURN8GHJ7MRWW3UXYMM59E3K7MF0D3H82UNV9ACXZ7FLW4EK2UNFVS7NXWPKXSURYYAF0CA Create alias Add the following to your .bash_profile
Read moreCorrecting typos in a word based on the frequency dictionary
Correcting typos in a word based on the frequency dictionary. This algorithm is based on the distance between words according to the Levenshtein-Damerau algorithm, and the frequency of words in the language. There is a choice of three functions for calculating the best option: linear, exponential and exponential. The parameters for these functions are user-definable. 05.12.2021 Python 3.9.4 Package: csv, logging, typing Author: https://github.com/anton2yakovlev GitHub View Github
Read moreFinding and fixing bugs with deep learning
Finding and fixing bugs in code is a time-consuming, and often frustrating, part of everyday work for software developers. Can deep learning address this problem and help developers deliver better software, faster? In a new paper, Self-Supervised Bug Detection and Repair, presented at the 2021 Conference on Neural Information Processing Systems (NeurIPS 2021), we show a promising deep learning model, which we call BugLab. BugLab can be taught to detect and fix bugs, without using labelled data, through a “hide […]
Read more
Interpretable agent communication from scratch (with a generic visual processor emerging on the side)
Abstract As deep networks begin to be deployed as autonomous agents, the issue of how they can communicate with each other becomes important. Here, we train two deep nets from scratch to perform large-scale referent identification through unsupervised emergent communication. We show that the partially interpretable emergent protocol allows the nets to successfully communicate even about object classes they did not see at training time. The visual representations induced as a by-product of our training regime, moreover, when re-used as […]
Read more
Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling
Abstract Multi-head attention has each of the attention heads collect salient information from different parts of an input sequence, making it a powerful mechanism for sequence modeling. Multilingual and multi-domain learning are common scenarios for sequence modeling, where the key challenge is to maximize positive transfer and mitigate negative interference across languages and domains. In this paper, we find that non-selective attention sharing is sub-optimal for achieving good generalization across all languages and domains. We further propose attention sharing strategies […]
Read more