NASAARSET /
ARSET_ML_Fundamentals
Repository for Jupyter Notebook examples associated with the NASA ARSET Training, "Fundamentals of Machine Learning for Earth Science"
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parkerg1952 / repository
A repository for working with project organization strategies in the context of remote sensing and jupyter notebooks.
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This is a development repository for the NEON Data Skills portal. Here the NEON Data Skills team develops and builds new content. Content is transferred to the NEONScience/NEON-Data-Skills repository to be published.
This directory will contain all the code related to the Remote Sensing Python lessons originally created for Data Institute 2017.
*images/Remote-Sensing-Python/...: each series/lesson will have a subdirectory with the same naming/file
path as in the _posts directory. This allows for organized and automated
creation of files and embedding the images in the files.
code/Remote-Sensing-Python/...: this directory (same file paths inside) will contain any code that
should be downloadable with the lesson via the "Download code" button at the
bottom of each tutorial page.The Day 1 Lesson (Jupyter Notebooks) were designed in the following order:
Data, once uploaded should be stored under the Remote-Sensing-Python folder, or the paths to access data will need to be modified in the notebook scripts. I am still updating and adding functions to the neon_aop.py module that is loaded at the start of lessons 2-4, but all functions required to run the notebooks should already be uploaded.
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NASAARSET /
Repository for Jupyter Notebook examples associated with the NASA ARSET Training, "Fundamentals of Machine Learning for Earth Science"
81/100 healthStephanRhode /
This repository contains jupyter notebooks and python code for KIT course: Python Algorithms for Automotive Engineering
58/100 healthThis repository will contain the presentation and python jupyter notebooks for my DataHack Summit 2025 conference talk, Building Effective Agentic AI Systems: Lessons from the Field. Drawing from my experience building deploying Agentic AI systems, we’ll focus on three pillars: Architecting, Optimizing, and Observability for Agentic AI Systems.
ChaitanyaK77 /
This Repository provides a Jupyter Notebook for building a small language model from scratch using 'TinyStories' dataset. Covers data preprocessing, BPE tokenization, binary storage, GPU memory management, and training a Transformer in PyTorch. Generate sample stories to test your model. Ideal for learning NLP and PyTorch.
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This repository contains a several tutorials for using the ECCO Central Production Version 4 ocean and sea-ice state estimate. Individiual lessons are provided as both Juypter notebooks (Tutorials_as_Jupyter_Notebooks/ ) and Python file (Tutorials_as_Python_Files/).
48/100 healthopenworm /
A repository containing code for a number of tutorials on the Hodgkin Huxley model, including an interactive Jupyter notebook
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