Author: Deep Learner

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

Amazon Product review Sentiment Analysis using BERT

This article was published as a part of the Data Science Blogathon Introduction Natural Language processing, a sub-field of machine learning has gained immense popularity in the last 5 years in both research and industrial applications due to the advancement in the field of deep learning and improvement in the computational power of hardware systems. It is a technique for computers to understand how human languages work involving the usage of computational linguistics and the computer science domain. In recent years, […]

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Beginner, Machine Learning, NLP, Unstructured Data, Unsupervised

Topic Modeling and Latent Dirichlet Allocation(LDA) using Gensim and Sklearn : Part 1

This article was published as a part of the Data Science Blogathon Introduction Let’s say you have a client who has a publishing house. Your client comes to you with two tasks: one he wants to categorize all the books or the research papers he receives weekly on a common theme or a topic and the other task is to encapsulate large documents into smaller bite-sized texts. Is there any technique and tool available that can do both of these two […]

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Machine Learning

CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds

CloudAAE This is an tensorflow implementation of “CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds” Files log: directory to store log files during training. losses: loss functions for training. models: a python file defining model structure. object_model_tfrecord: full object models for data synthesizing and visualization purpose. tf_ops: tensorflow implementation of sampling operations (credit: Haoqiang Fan, Charles R. Qi). trained_network: a trained network. utils: utility files for defining model structure. ycb_video_data_tfRecords: synthetic training data and real […]

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Machine Learning

Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition

Patch-NetVLAD This repository contains code for the CVPR2021 paper “Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition” The article can be found on arXiv and the official proceedings. Installation We recommend using conda (or better: mamba) to install all dependencies. If you have not yet installed conda/mamba, please download and install mambaforge. conda create -n patchnetvlad python=3.8 numpy pytorch-gpu torchvision natsort tqdm opencv pillow scikit-learn faiss matplotlib-base -c conda-forge conda activate patchnetvlad We provide several pre-trained models and configuration […]

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Face recognition

A standard evaluation metric to measure the accuracy and robustness of 3D face reconstruction methods

NoW Evaluation This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard evaluation metric to measure the accuracy and robustness of 3D face reconstruction methods from a single image under variations in viewing angle, lighting, and common occlusions. Evaluation metric Given a single monocular image, the challenge consists of reconstructing a 3D face. Since the predicted meshes occur in different local coordinate systems, the reconstructed 3D mesh […]

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PyTorch

Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking

ArTIST Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking (CVPR 2021) Pytorch implementation of the ArTIST motion model. In this repo, there are Training script for the Moving Agent network Training script for the ArTIST motion model Demo script for Inferring the likelihood of current observations (detections) Demo script for Inpainting the missing observation/detections Demo 1: Likelihood estimation of observation Run: python3 demo_scoring.py This will generate the output in the temp/ar/log_p directory, look like this: This demo gets as […]

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Translation

Break-It-Fix-It: Learning to Repair Programs from Unlabeled Data

BIFI Break-It-Fix-It: Learning to Repair Programs from Unlabeled Data This repo provides the source code & data of our paper: Break-It-Fix-It: Unsupervised Learning for Program Repair (ICML 2021). @InProceedings{yasunaga2021break, author = {Michihiro Yasunaga and Percy Liang}, title = {Break-It-Fix-It: Unsupervised Learning for Program Repair}, year = {2021}, booktitle = {International Conference on Machine Learning (ICML)}, } Problem: Repair Task Our approach: BIFI 0. Dependencies Specifically, run the following commands to create a conda environment (assuming CUDA10.1): conda create -n BIFI […]

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Database

Migrate data from SQL to NoSQL easily with python

sql2nosql Migrate data from SQL to NoSQL easily. Installation 💯 pip install sql2nosql –upgrade Dependencies 📢 For the package to work, it first needs “clients”, which are other packages that are in charge of managing the data in the database. Most of them work very similar, as in the case of ‘mysql-connector’ and ‘pymysql’ for MySQL databases, and ‘PyMongo’ for MongoDB databases. For example, the parameter ‘sql_client’ of the Migrator() class, receives by parameter a string where it is indicated […]

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Text

Saliency-based Span Mixup for Text Classification

SSMix Saliency-based Span Mixup for Text Classification (Findings of ACL 2021) Abstract Data augmentation with mixup has shown to be effective on various computer vision tasks. Despite its great success, there has been a hurdle to apply mixup to NLP tasks since text consists of discrete tokens with variable length. In this work, we propose SSMix, a novel mixup method where the operation is performed on input text rather than on hidden vectors like previous approaches. SSMix synthesizes a sentence […]

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Framework

Framework that uses artificial intelligence applied to mathematical models to make predictions

LiconIA Framework that uses artificial intelligence applied to mathematical models to make predictions Requirements Python 3.6 or superior (sudo apt-get install python3.6 under Linux)Python virtual environment (sudo apt-get install python3.6-venv under Linux) Development and Tests Installing the package Start by creating a new virtual environment for your project. Next, update the packages pip and setuptools to the latest version. Then install the package itself. $ sudo apt-get install python3-tk $ /usr/bin/python3.6 -m venv –prompt=”LiconIA” venv $ source venv/bin/activate (LiconIA) $ […]

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