Author: Deep Learner

Machine Learning

A Python 3 package providing several time-efficient algorithms

AntroPy AntroPy is a Python 3 package providing several time-efficient algorithms for computing the complexity of time-series. It can be used for example to extract features from EEG signals. Installation pip install antropy Functions Entropy import numpy as np import antropy as ant np.random.seed(1234567) x = np.random.normal(size=3000) # Permutation entropy print(ant.perm_entropy(x, normalize=True)) # Spectral entropy print(ant.spectral_entropy(x, sf=100, method=’welch’, normalize=True)) # Singular value decomposition entropy print(ant.svd_entropy(x, normalize=True)) # Approximate entropy print(ant.app_entropy(x)) # Sample entropy print(ant.sample_entropy(x)) # Hjorth mobility and complexity print(ant.hjorth_params(x)) […]

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Text

A python interactive visualization tool for text summarization

SummVis Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization SummVis is an open-source visualization tool that supports fine-grained analysis of summarization models, data, and evaluation metrics. Through its lexical and semantic visualizations, SummVis enables in-depth exploration across important dimensions such as factual consistency and abstractiveness. Authors: Jesse Vig, Wojciech Kryściński, Karan Goel, Nazneen Fatema RajaniSalesforce Research Stanford Hazy Research User guide Overview SummVis is a tool for analyzing abstractive summarization systems. It provides fine-grained insights on summarizationmodels, […]

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Excel

Simple XLSX and CSV to dictionary converter with python

sheet2dict A simple XLSX/CSV reader – to dictionary converter Installing To install the package from pip, first run: python3 -m pip install –no-cache-dir sheet2dict Required pip packages for sheet2doc: csv, openpyxl Usage This library has 2 main features: reading a spreadsheet files and converting them to array of python dictionaries. – XLSX Use xlsx_to_dict() method when converting form spreadsheets.Supported file formats for spreadsheets are: .xlsx,.xlsm,.xltx,.xltm # Import the library from sheet2dict import Worksheet # Create an object ws = Worksheet() […]

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Machine Learning

Flexible interface for high performance research using SOTA Transformers

lightning-transformers Flexible interface for high-performance research using SOTA Transformers leveraging Pytorch Lightning, Transformers, and Hydra. Installation Option 1: from PyPI pip install lightning-transformers # instead of: `python train.py …`, run with: pl-transformers-train … Option 2: from source git clone https://github.com/PyTorchLightning/lightning-transformers.git cd lightning-transformers pip install . python train.py … # the `pl-transformers-train` endpoint is also available! Quick recipes Train bert-base-cased on the CARER emotion dataset using the Text Classification task. python train.py task=nlp/text_classification dataset=nlp/text_classification/emotion See the composed Hydra config used under-the-hood […]

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Machine Learning

Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation

Talking-Face_PC-AVS Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation (CVPR 2021) We propose Pose-Controllable Audio-Visual System (PC-AVS), which achieves free pose control when driving arbitrary talking faces with audios. Instead of learning pose motions from audios, we leverage another pose source video to compensate only for head motions. The key is to devise an implicit low-dimension pose code that is free of mouth shape or identity information. In this way, audio-visual representations are modularized into spaces of three key […]

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Command-line Tools

CommonMark compliant Markdown formatter with python

Mdformat Mdformat is an opinionated Markdown formatter that can be used to enforce a consistent style in Markdown files. Mdformat is a Unix-style command-line tool as well as a Python library. Installing Install with CommonMark support: pip install mdformat Alternatively install with GitHub Flavored Markdown (GFM) support: pip install mdformat-gfm Or with Markedly Structured Text (MyST) support: pip install mdformat-myst Command line usage Format files Format files README.md and CHANGELOG.md in place mdformat README.md CHANGELOG.md Format .md files in current […]

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Python

Python Basics: Paperback Now Available!

After years of writing, reviewing, and testing, we’re delighted to announce that Python Basics: A Practical Introduction to Python 3 is now available in paperback! It’s been rewarding to hear how readers have been using this book to supercharge their learning. After receiving so much community feedback, we’re confident that you’ll build a strong foundation for your Python journey with this book: You’ll not only cover the core concepts you really need to know, but you’ll also learn them in […]

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Uncategorized Leave a comment

Alexandria in Microsoft Viva Topics: from big data to big knowledge

Project Alexandria is a research project within Microsoft Research Cambridge dedicated to discovering entities, or topics of information, and their associated properties from unstructured documents. This research lab has studied knowledge mining research for over a decade, using the probabilistic programming framework Infer.NET. Project Alexandria was established seven years ago to build on Infer.NET and retrieve facts, schemas, and entities from unstructured data sources while adhering to Microsoft’s robust privacy standards. The goal of the project is to construct a […]

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NLP

MLOps Primer – 2021

Machine learning operations (MLOps) is becoming an exciting space as we figure out the best practices and technologies to deploy machine learning models in the real world. MLOps enable ML teams to build responsible and scalable machine learning systems and infrastructure. This facilitates tasks that range from risk assessment to building and testing to monitoring. While still in its infancy, MLOps has attracted machine learning engineers and software engineers in general. With every new paradigm comes new challenges and opportunities […]

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Neural Machine Translation (NMT), NMT Leave a comment

Machine Translation Weekly 76: Zero-shot MT with pre-trained encoder

Using pre-trained multilingual representation as a universal encoder for machine translation might seem like an obvious thing to try: train a decoder into one target language using one or several source languages and you get a translation from 100 languages into the target language. This sounds great, but this is not how it works. (Or it works somehow, but not really well, I tried it myself.) Recently, I came across a pre-print where the authors figured out how to do […]

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