Make NixOS usable for non-technical users through a settings / package management GUI

Nix-Gui Make NixOS usable for non-technical users through a settings / package management GUI. Motives The declarative nature of NixOS provides it the capability of being the most user friendly linux distro. No more editing dotfiles, /etc files, manually writing timers, services, running commands to manage and create users and groups, etc. NixOS integrates all of that into a declarative system, and this project integrates NixOS’ declarative system into a GUI. Serve Users Unfamiliar with or Learning Nix Nix-Gui is […]

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Push your spaCy pipelines to the Hugging Face Hub

spacy-huggingface-hub This package provides a CLI command for uploading any trained spaCy pipeline packaged with spacy package to the Hugging Face Hub. It auto-generates all meta information for you, uploads a pretty README (requires spaCy v3.1+) and handles version control under the hood. About the Hugging Face Hub The Hugging Face Hub hosts Git-based repositories which are storage spaces that can contain all your files. These repositories have multiple advantages: versioning (commit history and diffs), branches, useful metadata about their […]

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A fast and feature-rich CTC beam search decoder for speech recognition with python

pyctcdecode A fast and feature-rich CTC beam search decoder for speech recognition written in Python, providing n-gram (kenlm) language model support similar to PaddlePaddle’s decoder, but incorporating many new features such as byte pair encoding and real-time decoding to support models like Nvidia’s Conformer-CTC or Facebook’s Wav2Vec2. pip install pyctcdecode Main Features: 🔥 hotword boosting 🤖 handling of BPE vocabulary 👥 multi-LM support for 2+ models 🕒 stateful LM for real-time decoding ✨ native frame index annotation of words 💨 fast runtime, comparable to C++ implementation […]

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Unofficial implementation of Alias-Free Generative Adversarial Networks

alias-free-gan-pytorch Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) This implementation contains a lot of my guesses, so I think there are many differences to the official implementations Usage First create lmdb datasets: python prepare_data.py –out LMDB_PATH –n_worker N_WORKER –size SIZE1,SIZE2,SIZE3,… DATASET_PATH This will convert images to jpeg and pre-resizes it. This implementation does not use progressive growing, but you can create multiple resolution datasets using size arguments with comma separated lists, for the cases that you want to try […]

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A CTF web challenge about making screenshots

screenshotter (web) A CTF web challenge about making screenshots. It is inspired by a bug found in real life. Run the challenge To run the challenge you have to install docker-compose: docker-compose up Once the servicses are running, you should be able to access http://127.0.0.1:5000. Solution The DO_NOT_ACCESS folder contains the solution when you checkout the solution branch with git checkout solution.    

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Custom Text Classification on Android using TensorFlow Lite

This article was published as a part of the Data Science Blogathon Introduction A lot of social media platforms have been using AI these days to classify vulgar and offensive posts and automatically take them down. I thought why not try doing something similar; and so, I’ve come up with this end-to-end tutorial that will help you build your own corpus for training a text classification model, and later export and deploy it on an Android app for you to use. […]

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LSTM for Text Classification in Python

This article was published as a part of the Data Science Blogathon With an emerging field of deep learning, performing complex operations has become faster and easier. As you start exploring the field of deep learning, you are definitely going to come across words like Neural networks, recurrent neural networks, LSTM, GRU, etc. This article explains LSTM and its use in Text Classification. So what is LSTM? And how can it be used? What is LSTM? LSTM stands for Long-Short Term […]

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Build your own AI chatbot from scratch!

This article was published as a part of the Data Science Blogathon Introduction It’s pretty simple! Today we will learn to create an AI chatbot from scratch using Intent matching and NLP algorithms. Let’s see what we are gonna do: * Prepare our dataset with questions(keywords) and respective intents. * Prepare a JSON file containing replies for each intent. * Transform our data into Tf-Idf Vectors. * Use Deep Neural Network to classify the User’s question into one of the intents […]

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NLP: Answer Retrieval from Document using Python

This article was published as a part of the Data Science Blogathon Introduction → This article focuses on answer retrieval from a document by using similarity and difference metrics. This task falls under Natural Language Processing which is a subset of Deep Learning. In this article we will be understanding the concept of general similarity algorithms and how can they be applied to complete our task. The article will be based on python for the coding part. How to Approach → To […]

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Resume Screening with Natural Language Processing in Python

For each recruitment, companies take out online ads, referrals and go through them manually. Companies often submit thousands of resumes for every posting. When companies collect resumes through online advertisements, they categorize those resumes according to their requirements. After collecting resumes, companies close advertisements and online applying portals. Then they send the collected resumes to the Hiring Team(s). It becomes very difficult for the hiring teams to read the resume and select the resume according to the requirement, there is […]

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