Json

A simple and extensible JSON encoder/decoder for Python

simplejson simplejson is a simple, fast, complete, correct and extensible JSON http://json.org encoder and decoder for Python 3.3+ with legacy support for Python 2.5+. It is pure Python code with no dependencies, but includes an optional C extension for a serious speed boost. simplejson is the externally maintained development version of the json library included with Python (since 2.6). This version is tested with the latest Python 3.8 and maintains backwards compatibility with Python 3.3+ and the legacy Python 2.5 […]

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Emojis

Emoji terminal output for Python

Emoji Emoji for Python. This project was inspired by kyokomi. Example The entire set of Emoji codes as defined by the unicode consortium is supported in addition to a bunch of aliases. By default, only the official list is enabled but doing emoji.emojize(use_aliases=True) enables both the full list and aliases. >> import emoji >> print(emoji.emojize(‘Python is :thumbs_up:’)) Python is đź‘Ť >> print(emoji.emojize(‘Python is :thumbsup:’, use_aliases=True)) Python is đź‘Ť >> print(emoji.demojize(‘Python is đź‘Ť’)) Python is :thumbs_up: >>> print(emoji.emojize(“Python is fun :red_heart:”)) […]

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Text

Predict an emoji that is associated with a text

Sentiment Analysis Sentiment analysis in computational linguistics is a general term for techniques that quantify sentiment or mood in a text. Can you tell from a text whether the writer is happy? Angry? Disappointed? Can you put their happiness on a 1-5 scale? Robust tools for sentiment analysis are often very desirable for companies, for example. Imagine that a company has just launched a new product GizmoX. Now the management wants to know how customers feel about it. Instead of […]

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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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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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