Raspberrypi

Monorepo for my Raspberry Pi dashboard and GPS satellite listener

pi dashboard Monorepo for my Raspberry Pi dashboard and GPS satellite listener. It includes: a module system allows different features to be toggled on and off so you don’t need my exact hardware setup. It also allows multiple Pis to use the same server. :house: a Windows 98 themed dashboard. :satellite: view the list of visible GPS satellites from a GPS hardware device. :printer: receive printer messages to a POS58 compatible printer. :thermometer: live temperature/humidity collected from an AMxx compatible […]

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Machine Learning

A python library that lets you customize automated machine learning

nylon An intelligent, flexible grammar of machine learning. Nylon is a python library that lets you customize automated machine learning workflows through a concise, JSON syntax. It provides a built in grammar, in which you can access different operations in ML with the english language. Installation Install latest release version: pip install -U nylon-ai Install directory from github: git clone https://github.com/Palashio/nylon.git cd nylon-ai pip install . Usage: the basics A new Polymer object should be created everytime you’re working with […]

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PyTorch

A variety of sequence model architectures from scratch in PyTorch

Sequence Models This repository implements a variety of sequence model architectures from scratch in PyTorch. Effort has been put to make the code well structured so that it can serve as learning material. The training loop implements the learner design pattern from fast.ai in pure PyTorch, with access to the loop provided through callbacks. Detailed logging and graphs are also provided with python logging and wandb. Additional implementations will be added. Setup Using Miniconda/Anaconda: cd path_to_repo conda create –name –file […]

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Machine Learning

RGB-D Local Implicit Function for Depth Completion of Transparent Objects

implicit_depth This repository maintains the official implementation of our CVPR 2021 paper: RGB-D Local Implicit Function for Depth Completion of Transparent Objects By Luyang Zhu, Arsalan Mousavian, Yu Xiang, Hammad Mazhar, Jozef van Eenbergen, Shoubhik Debnath, Dieter Fox Requirements The code has been tested on the following system: Ubuntu 18.04 Nvidia GPU (4 Tesla V100 32GB GPUs) and CUDA 10.2 python 3.7 pytorch 1.6.0 Installation Docker (Recommended) We provide a Dockerfile for building a container to run our code. More […]

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Jupyter notebooks

Manage python virtual environments on the working notebook server

notebook-environments Manage python virtual environments on the working notebook server. Installation It is recommended to use this package together with virtualenv and virtualenvwrapper to work with python virtual environments more suitable. Make sure the installed python interpreters work without errors on the current operating system. To install this package as a standalone application with the command-line interface you are to run the following command: sudo sh -c “$(curl https://raw.githubusercontent.com/vladpunko/notebook-environments/master/install.sh)” Use the package manager pip to install notebook-environments without the command-line […]

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Deep Learning, Libraries, NLP, Python, Unsupervised

Implementing Transformers in NLP Under 5 Lines Of Codes

This article was published as a part of the Data Science Blogathon Introduction Today, we will see a gentle introduction to the transformers library for executing state-of-the-art models for complex NLP tasks. Applying state-of-the-art Natural Language Processing models has never been more straightforward. Hugging Face has revealed a compelling library called transformers that allow us to perform and use a broad class of state-of-the-art NLP models in a specific way. Today we are operating to install and use the transformers library […]

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Advanced, NLP, Project, Python, Structured Data, Text

Text Preprocessing made easy!

This article was published as a part of the Data Science Blogathon Introduction We will learn the basics of text preprocessing in this article. Humans communicate using words and hence generate a lot of text data for companies in the form of reviews, suggestions, feedback, social media, etc. A lot of valuable insights can be generated from this text data and hence companies try to apply various machine learning or deep learning models to this data to gain actionable insights. Text […]

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Advanced, NLP, Project, Python, Structured Data, Text

NLP Application: Named Entity Recognition (NER) in Python with Spacy

Natural Language Processing deals with text data. The amount of text data generated these days is enormous. And, this data if utilized properly can bring many fruitful results. Some of the most important Natural Language Processing applications are Text Analytics, Parts of Speech Tagging, Sentiment Analysis, and Named Entity Recognition. The vast amount of text data contains a huge amount of information. An important aspect of analyzing these text data is the identification of Named Entities. What is a Named […]

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Advanced, Data Science, NLP, Python, Text

A Gentle Introduction To MuRIL : Multilingual Representations for Indian Languages

This article was published as a part of the Data Science Blogathon “MuRIL is a starting point of what we believe can be the next big evolution for Indian language understanding. We hope it will prove to be a better foundation for researchers, startups, students, and anyone else interested in building Indian language technologies” said Partha Talukdar, Research Scientist, Google Research India. What is MuRIL? MuRIL, short for Multilingual Representations for Indian Languages, is none other than a free and open-source […]

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Advanced, NLP, Programming, Project, Python, Supervised, Technique, Text, Use Cases

TS-SS similarity for Answer Retrieval from Document in 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 a similarity algorithm. This task falls under Natural Language Processing which is a subset of Deep Learning. In this article, we will be understanding why do we require better techniques and what are the drawbacks of using naive algorithms. Moreover, we will be implementing a similarity-based technique for answer retrieval from the document. This article is a […]

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