Jupyter notebooks

Python code to crawl computer vision papers from top CV conferences

News 2021-6-21 Support CVPR-2021 Download all CVPR-2021 papers in one click. Just set the local download directory in download_cvpr2021.py and run it! Don’t forget to have your chrome driver ready (i.e., corresponding version to your Chrome browser) 2021-6-20 Support continuation of downloading from where the program encounters interruption. (prevent re-downloading from scratch) Introduction Python code to crawl computer vision papers from top CV conferences. Currently it supports CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, SIGGRAPH. It leverages selenium, a website testing […]

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Data Analysis

A toolkit to analyze time series data with python

Kats Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis. Time series analysis is an essential component of Data Science and Engineering work at industry, from understanding the key statistics and characteristics, detecting regressions and anomalies, to forecasting future trends. Kats aims to provide the one-stop shop for time series analysis, including detection, forecasting, feature extraction/embedding, multivariate analysis, etc. Kats is released by Facebook’s Infrastructure Data Science team. It […]

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Machine Learning

Python code for Machine learning: a probabilistic perspective

pyprobml Python 3 code for my new book series Probabilistic Machine Learning. This is work in progress, so expect rough edges. Getting less rough… Jupyter notebooks For each chapter there are one or more accompanying Jupyter notebooks that cover some of the material in more detail.When you open a notebook, there will be a button at the top that says ‘Open in colab’. If you click on this, it will start a virtual machine (VM) instance on Google Cloud Platform […]

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Python

Python vs JavaScript for Python Developers

Python isn’t the only language out there, and one of the other languages frequently fighting Python for the top of the “most popular” lists is JavaScript. JavaScript is the de facto language on the web but also has a robust toolset on the server side. This course explores JavaScript from a Python programmer’s perspective. If you’ve never used JavaScript before or have felt overwhelmed by the quick pace of its evolution in recent years, then this course will set you […]

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Advanced, NLP, Project, Python, Text

Part 1: Step by Step Guide to Master NLP – Introduction

This article was published as a part of the Data Science Blogathon Introduction Computers and Machines are great while working with tabular data or Spreadsheets. However, human beings generally communicate in words and sentences, not in the form of tables or spreadsheets, and most of the information that humans speak or write is present in an unstructured manner. So it is not very understandable for computers to interpret these languages. Therefore, In natural language processing (NLP), our aim is to make […]

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Beginner, Data Cleaning, NLP, Python, Text

Part 4: Step by Step Guide to Master NLP – Text Cleaning Techniques

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). In the previous part of this blog series, we complete the initial steps involved in text cleaning and preprocessing that are related to NLP. Now, in continuation of that part, in this article, we will cover the next techniques involved in the NLP pipeline of Text preprocessing. In this article, we will first discuss […]

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Advanced, Computer Vision, Image, Image Analysis, NLP, Project, Python, Unstructured Data

Text detection from images using EasyOCR: Hands-on guide

# Changing the image path IMAGE_PATH = ‘Turkish_text.png’ # Same code here just changing the attribute from [‘en’] to [‘zh’] reader = easyocr.Reader([‘tr’]) result = reader.readtext(IMAGE_PATH,paragraph=”False”) result Output: [[[[89, 7], [717, 7], [717, 108], [89, 108]], ‘Most Common Texting Slang in Turkish’], [[[392, 234], [446, 234], [446, 260], [392, 260]], ‘test’], [[[353, 263], [488, 263], [488, 308], [353, 308]], ‘yazmak’], [[[394, 380], [446, 380], [446, 410], [394, 410]], ‘link’], [[[351, 409], [489, 409], [489, 453], [351, 453]], ‘bağlantı’], [[[373, 525], […]

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Jupyter notebooks

UmlsBERT: Augmenting Contextual Embeddings with a Clinical Metathesaurus

UmlsBERT UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus General info This is the code that was used of the paper : UmlsBERT: Augmenting Contextual Embeddings with a Clinical Metathesaurus (NAACL 2021). In this work, we introduced UmlsBERT, a contextual embedding model capable of integrating domain knowledge during pre-training. It was trained on biomedical corpora and uses the Unified Medical Language System (UMLS) clinical metathesaurus in two ways: We proposed a new multi-label […]

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Machine Learning

PyTorch implementation for Graph Contrastive Learning Automated

Graph Contrastive Learning Automated PyTorch implementation for Graph Contrastive Learning Automated . Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang In ICML 2021. Overview In this repository, we propose a principled framework named joint augmentation selection (JOAO), to automatically, adaptively and dynamically select augmentations during GraphCL training.Sanity check shows that the selection aligns with previous “best practices”, as shown in Figure 2. Dependencies Experiments Citation If you use this code for you research, please cite our paper. @article{you2021graph, title={Graph Contrastive […]

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Graph

Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction

Neural Deformation Graphs Neural Deformation Graphs for Globally-consistent Non-rigid ReconstructionAljaž Božič, Pablo Palafox, Michael Zollhöfer, Justus Thies, Angela Dai, Matthias NießnerCVPR 2021 (Oral Presentation) This repository contains the code for the CVPR 2021 paper Neural Deformation Graphs, a novel approach for globally-consistent deformation tracking and 3D reconstruction of non-rigid objects. Specifically, we implicitly model a deformation graph via a deep neural network and empose per-frame viewpoint consistency as well as inter-frame graph and surface consistency constraints in a self-supervised fashion. […]

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