petroimanol's repositories
advertools
advertools - online marketing productivity and analysis tools
Awesome-Deep-Learning-Resources
Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier
Cookbook
The Data Engineering Cookbook
Cracking-the-Coding-Interview_solutions
Efficient solutions to "Cracking the Coding Interview" (6th Edition) problems
CT-GAN
A GAN based framework for adding and removing medical evidence in 3D volumetric medical scans
deepschool.io
Deep Learning tutorials in jupyter notebooks.
english-words
:memo: A text file containing 479k English words for all your dictionary/word-based projects e.g: auto-completion / autosuggestion
EvalNE
Source code for EvalNE, a Python library for evaluating Network Embedding methods.
fake-news
Fake news detection
FakeNewsNet
This is a dataset for fake news detection research
gans
GANs in slanted land
HackerRank_solutions
317 efficient solutions to HackerRank problems
HAR-stacked-residual-bidir-LSTMs
Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.
Kata-Clean-Machine-Learning-From-Dirty-Code
A coding exercise: let's convert dirty machine learning code into clean code using a Pipeline - which is the Pipe and Filter Design Pattern applied to Machine Learning.
LeetCode_solutions
203 efficient solutions to LeetCode problems
lightweight-gan
Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two
Linear-Inverse-RL-algorithms
Implementation of Linear Inverse Reinforcement Learning Algorithm (IRL) on Mountain Car Environment.
LSTM-Human-Activity-Recognition
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier
pycon_social_networkx
Social network analyses code examples for PyCon 2019 talk
Scweet
A simple and unlimited twitter scraper : scape tweets, likes, retweets, following, followers, user info, images...
SentimentalLIAR
Our Sentimental LIAR dataset is a modified and further extended version of the LIAR extension introduced by Kirilin et al. In our dataset, the multi-class labeling of LIAR is converted to a binary annotation by changing half-true, false, barely-true and pants-fire labels to False, and the remaining labels to True. Furthermore, we convert the speaker names to numerical IDs in order to avoid bias with regards to the textual representation of names. The binary-label dataset is then extended by adding sentiments derived using the Google NLP API . Sentiment analysis determines the overall attitude of the text (i.e., whether it is positive or negative), and is quantified by a numerical score. If the sentiment score is positive, then we assign Positive for the sentiment attribute, otherwise Negative is assigned. We also introduced a further extension by adding emotion scores extracted using the IBM NLP API for each claim, which determine the detected level of 6 emotional states namely anger, sadness, disgust, fear and joy. The score for each emotion is between the range of 0 and 1. Table I demonstrates a sample record in Sentimental LIAR for a short claim in the LIAR dataset This repository contains the dataset for this paper: https://arxiv.org/abs/2009.01047
stylegan2
StyleGAN2 - Official TensorFlow Implementation
stylegan2-pytorch
Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
tutorials_week2
tutorials for MLSS 2019 Skoltech
twitter_api
Tutorial on how to use the Twitter Dev APIs to collect tweets
TwitterScraper
Repository containing all files relevant to my basic and advanced tweet scraping articles.
vaderSentiment
VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.