joolsa
Associate Professor at the University of Vaasa. Research in marketing and HCI. https://jonisalminen.com
University of VaasaVaasa, Finland
joolsa's Stars
microsoft/ML-For-Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
dair-ai/Prompt-Engineering-Guide
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
jessevig/bertviz
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
uber/causalml
Uplift modeling and causal inference with machine learning algorithms
facebookresearch/Kats
Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.
makcedward/nlpaug
Data augmentation for NLP
life4/textdistance
📐 Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.
LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words
List of Dirty, Naughty, Obscene, and Otherwise Bad Words
bazingagin/npc_gzip
Code for Paper: “Low-Resource” Text Classification: A Parameter-Free Classification Method with Compressors
jasonwei20/eda_nlp
Data augmentation for NLP, presented at EMNLP 2019
microsoft/prompts-for-edu
Hello-SimpleAI/chatgpt-comparison-detection
Human ChatGPT Comparison Corpus (HC3), Detectors, and more! 🔥
google/lightweight_mmm
LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.
patcg-individual-drafts/topics
The Topics API
THU-KEG/EvaluationPapers4ChatGPT
Resource, Evaluation and Detection Papers for ChatGPT
google/ads-privacy
zacanger/profane-words
A very long list of English profanity.
savasy/Turkish-Bert-NLP-Pipeline
Bert-base NLP pipeline for Turkish, Ner, Sentiment Analysis, Question Answering etc.
euagendas/m3inference
A deep learning system for demographic inference (gender, age, and individual/person) that was trained on massive Twitter dataset using profile images, screen names, names, and biographies
google-research-datasets/Synthetic-Persona-Chat
The Synthetic-Persona-Chat dataset is a synthetically generated persona-based dialogue dataset. It extends the original Persona-Chat dataset.
AntonsRuberts/datascience_marketing
Here I'll publish all of my personal projects that relate to Data Science in Marketing
vincentkoc/synthetic-user-research
Example Notebook for Synthetic User Research with Persona Prompting and Autonomous Agents
yoonhwang/hands-on-data-science-for-marketing
joferkington/oost_paper_code
Scripts and software for out-of-sequence shortening calculations
mlr-org/mlr3cluster
Cluster analysis for mlr3
jm-contreras/data-science-management-resources
A list of resources for current and aspiring data science managers
aberke/floc-analysis
FLoC analysis: privacy for sensitive groups
andrgig/Data-Science-for-Management
Data Science for Management course
ankitachatterjee94/Competition-codes
SiriusFoundation/PopularityDebiasingWithPrivacy