froukje

froukje's Stars

• DataTalksClub/data-engineering-zoomcamp

  Free Data Engineering course!

  Language:Jupyter Notebook24.9k4561255.3k

• HarisIqbal88/PlotNeuralNet

  Latex code for making neural networks diagrams

  Language:TeX22k2271222.9k

• PrefectHQ/prefect

  Prefect is a workflow orchestration framework for building resilient data pipelines in Python.

  Language:Python16.1k1615.6k1.6k

• DataTalksClub/mlops-zoomcamp

  Free MLOps course from DataTalks.Club

  Language:Jupyter Notebook11.1k185932.1k

• DataTalksClub/machine-learning-zoomcamp

  Learn ML engineering for free in 4 months!

  Language:Jupyter Notebook9.5k1581562.2k

• actions/starter-workflows

  Accelerating new GitHub Actions workflows

  Language:TypeScript9.1k4565055.3k

• alexeygrigorev/data-science-interviews

  Data science interview questions and answers

  Language:HTML8.9k226162k

• xuebinqin/U-2-Net

  The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."

  Language:Python8.6k1423421.5k

• bentoml/BentoML

  The easiest way to serve AI apps and models - Build reliable Inference APIs, LLM apps, Multi-model chains, RAG service, and much more!

  Language:Python7.1k781.1k784

• jeffheaton/t81_558_deep_learning

  T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis

  Language:Jupyter Notebook5.7k3231253k

• evidentlyai/evidently

  Evidently is ​​an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

  Language:Jupyter Notebook5.3k48393592

• DataTalksClub/llm-zoomcamp

  LLM Zoomcamp - a free online course about building a Q&A system

  Language:Jupyter Notebook2.8k604454

• dask/dask-tutorial

  Dask tutorial

  Language:Jupyter Notebook1.8k90104701

• BloodAxe/pytorch-toolbelt

  PyTorch extensions for fast R&D prototyping and Kaggle farming

  Language:Python1.5k2833120

• sgrvinod/a-PyTorch-Tutorial-to-Super-Resolution

  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | a PyTorch Tutorial to Super-Resolution

  Language:Python6181816112

• zgana/fpp3-python-readalong

  Python-centered read-along of Forecasting: Principles and Practice

  Language:Jupyter Notebook46473150

• kleok/FLOODPY

  Flood Python Toolbox

  Language:Jupyter Notebook16983432

• bentoml/gallery

  BentoML Example Projects 🎨

  Language:Python1346950

• fraibacas/prefect-orion

  Language:Shell734520

• outerbounds/full-stack-ML-metaflow-tutorial

  It's all in the name

  Language:HTML736240

• ridvansalihkuzu/hyperview_eagleeyes

  Soil parameter estimation from hyperspectral satellite images

  Language:Jupyter Notebook20209

• DKRZ-AIM/cygnss-deployment

  Web Interface for Wind Speed Prediction

  Language:Jupyter Notebook18400

• gerrykou/docker_intro

  Introduction to Docker

  Language:Python8100

• DKRZ-AIM/mardata-training

  Language:Jupyter Notebook6203

• DKRZ-AIM/ai4eo-hyperview

  AI4EO Hyperview Machine Learning Challenge

  Language:Jupyter Notebook3310

• DKRZ-AIM/d4g-tutorial

  Machine learning tutorial for remote sensing scientists. We follow the lifecycle of a machine learning project with the example of predicting wind speed from CyGNSS data. Developed for the D4G Remote Sensing Workshop, GFZ, Potsdam, June 2022.

  Language:Jupyter Notebook2000