Month: May 2021

Emojis

Train emoji embeddings based on emoji descriptions

emoji2vec This is my attempt to train, visualize and evaluate emoji embeddings as presented by Ben Eisner, Tim Rocktäschel, Isabelle Augenstein, Matko Bošnjak, and Sebastian Riedel in their paper. Most of their results are used here to build an equivalently robust model in Keras, including the rather simple training process which is solely based on emoji descriptions, but instead of using word2vec (as it was originally proposed) this version uses global vectors. Overview src/ contains the code used to process […]

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DevOps Tools

The Universal Character Encoding Detector For Python

Chardet Python character encoding detector. Detects ASCII, UTF-8, UTF-16 (2 variants), UTF-32 (4 variants) Big5, GB2312, EUC-TW, HZ-GB-2312, ISO-2022-CN (Traditional and Simplified Chinese) EUC-JP, SHIFT_JIS, CP932, ISO-2022-JP (Japanese) EUC-KR, ISO-2022-KR, Johab (Korean) KOI8-R, MacCyrillic, IBM855, IBM866, ISO-8859-5, windows-1251 (Cyrillic) ISO-8859-5, windows-1251 (Bulgarian) ISO-8859-1, windows-1252 (Western European languages) ISO-8859-7, windows-1253 (Greek) ISO-8859-8, windows-1255 (Visual and Logical Hebrew) TIS-620 (Thai) Note Our ISO-8859-2 and windows-1250 (Hungarian) probers have been temporarily disabled until we can retrain the models. Requires Python 3.6+. Installation Install […]

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Uncategorized Leave a comment

CHI 2021: Making remote and hybrid meetings work in the new future of work

Over the course of the COVID-19 pandemic, some truths about the nature of work have been underscored: it is uniquely complex, quickly shifting, and increasingly technology-mediated. Teaching, medicine, mental health, and other professions—previously thought to be near-impossible to do remotely—have all abruptly moved to online and hybrid mediums. All kinds of workers have needed to find new and creative ways to do their jobs. For many, the boundary between office and home have become a thin, blurred line. These changes […]

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Python

Docker can slow down your code and distort your benchmarks

One of the benefits of containers over virtual machines is that you get some measure of isolation without the performance overhead or distortion of virtualization. Docker images therefore seem like a good way to get a reproducible environment for measuring CPU performance of your code. There are, however, complications. Sometimes, running under Docker can actually slow down your code and distort your performance measurements. On macOS and Windows, for example, standard Linux-based Docker containers aren’t actually running directly on the […]

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NLP, shared-private bilingual word embeddings

Issue #130 – Shared-Private Bilingual Word Embeddings for NMT

13 May21 Issue #130 – Shared-Private Bilingual Word Embeddings for NMT Author: Akshai Ramesh, Machine Translation Scientist @ Iconic Introduction In recent years, there has been a significant amount of research to improve the representation learning of neural machine translation (NMT). In today’s blog post, we will look at the work of Liu et al., 2019 who propose a novel approach called Shared-Private Bilingual Word Embeddings, to improve the word representations of NMT. Introduction A word representation is a mathematical […]

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Computer Vision

A Computer vision package that makes its easy to run Image processing

CVZone This is a Computer vision package that makes its easy to run Image processing and AI functions. At the core it uses OpenCV and Mediapipe libraries. Installation You can simply use pip to install the latest version of cvzone. pip install cvzone 60 FPS Face Detection import cvzone import cv2 cap = cv2.VideoCapture(0) detector = cvzone.FaceDetector() while True: success, img = cap.read() img, bboxs = detector.findFaces(img) print(bboxs) cv2.imshow(“Image”, img) cv2.waitKey(1) Hand Tracking Basic Code Example import cvzone import cv2 […]

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Machine Learning

A Lucid Framework for Transparent and Interpretable Machine Learning Models

lucidmode lucidmode is an open-source, low-code and lightweight Python framework for transparent and interpretable machine learning models. It has built in machine learning methods optimized for visual interpretation of some of the most relevant calculations. Installation With package manager (coming soon) Install by using pip package manager: pip install lucidmode Clone entire github project [email protected]:lucidmode/lucidmode.git and then install dependencies pip install -r requirements.txt Models Artificial Neural Network Feedforward Multilayer perceptron with backpropagation. fit: Fit model to data predict: Prediction according […]

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Command-line Tools

The command line interface for Gradient

Gradient CLI Gradient is an an end-to-end MLOps platform that enables individuals and organizations to quickly develop, train, and deploy Deep Learning models. The Gradient software stack runs on any infrastructure e.g. AWS, GCP, on-premise and low-cost Paperspace GPUs. Leverage automatic versioning, distributed training, built-in graphs & metrics, hyperparameter search, GradientCI, 1-click Jupyter Notebooks, our Python SDK, and more. Key components: Notebooks: 1-click Jupyter Notebooks. Experiments: Run experiments from a web interface, CLI, SDK, or GradientCI bot. Models: Store, analyze, […]

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Database

A PostgreSQL or SQLite orm for Python

Prom An opinionated lightweight orm for PostgreSQL or SQLite. Prom has been used in both single threaded and multi-threaded environments, including environments using Greenthreads. 1 Minute Getting Started with SQLite First, install prom: $ pip install prom Set an environment variable: $ export PROM_DSN=sqlite://:memory: Start python: $ python Create a prom Orm: >>> import prom >>> >>> class Foo(prom.Orm): … table_name = “foo_table_name” … bar = prom.Field(int) … >>> Now go wild and create some Foo objects: >>> for x […]

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HTTP

HTTP client mocking tool for Python

HTTPretty HTTP Client mocking tool for Python created by Gabriel Falcão . It provides a full fake TCP socket module. Inspired by FakeWeb Install pip install httpretty Common Use Cases Test-driven development of API integrations Fake responses of external APIs Record and playback HTTP requests Simple Example import sure import httpretty import requests @httpretty.activate def test_httpbin(): httpretty.register_uri( httpretty.GET, “https://httpbin.org/ip”, body='{“origin”: “127.0.0.1”}’ ) response = requests.get(‘https://httpbin.org/ip’) response.json().should.equal({‘origin’: ‘127.0.0.1’}) httpretty.latest_requests().should.have.length_of(1) httpretty.last_request().should.equal(httpretty.latest_requests()[0]) httpretty.last_request().body.should.equal(‘{“origin”: “127.0.0.1”}’) checking multiple responses @httpretty.activate def test_post_bodies(): url = ‘http://httpbin.org/post’ […]

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