vivianah
masters student @uchicago | dssg alum @datascifellows & @dssgturing | @umich @umsi grad :)
Chicago
vivianah's Stars
janishar/mit-deep-learning-book-pdf
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
openai/CLIP
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
aleju/imgaug
Image augmentation for machine learning experiments.
mlflow/mlflow
Open source platform for the machine learning lifecycle
microsoft/LightGBM
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
afshinea/stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning
oxford-cs-deepnlp-2017/lectures
Oxford Deep NLP 2017 course
huggingface/datasets
🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
JaidedAI/EasyOCR
Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
academic/awesome-datascience
:memo: An awesome Data Science repository to learn and apply for real world problems.
trekhleb/homemade-machine-learning
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
sebastianruder/NLP-progress
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
eriklindernoren/ML-From-Scratch
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
eugeneyan/applied-ml
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
ageron/handson-ml3
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
ageron/handson-ml
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
Avik-Jain/100-Days-Of-ML-Code
100 Days of ML Coding
dair-ai/Prompt-Engineering-Guide
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
gerdm/prml
Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop
TheAlgorithms/Jupyter
The repository contains script and notebook related to Statistics, Machine learning, Neural network, Deep learning, NLP, Numerical methods, and Automation.
benfred/implicit
Fast Python Collaborative Filtering for Implicit Feedback Datasets
KeithGalli/matplotlib_tutorial
Source code to go along with my tutorial to learn data visualization with the matplotlib library of Python
instillai/machine-learning-course
:speech_balloon: Machine Learning Course with Python:
pdm-project/pdm
A modern Python package and dependency manager supporting the latest PEP standards
cli-guidelines/cli-guidelines
A guide to help you write better command-line programs, taking traditional UNIX principles and updating them for the modern day.
emeli-dral/postgres_push_example
An example of data monitoring setup using Evidently tool, Postgres DG and Grafana service.
thomasnield/oreilly_data_engineering_fundamentals
justmarkham/scikit-learn-videos
Jupyter notebooks from the scikit-learn video series
unpingco/Python-for-Probability-Statistics-and-Machine-Learning
Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"
shreyashankar/ml-dataval-tutorial
Tutorial: Data Validation for Machine Learning Techniques