Jupyter Notebooks
Showing 34 of 106 projects
A free Google Colab-based toolbox with Jupyter notebooks and GUI for applying deep learning to microscopy data without coding expertise.
A fast, interactive datagrid widget for Jupyter Notebook and JupyterLab with advanced features like selections, renderers, and conditional formatting.
A collection of TensorFlow practice exercises covering fundamental machine learning concepts from linear regression to CNNs.
A VS Code extension for visually exploring, cleaning, and transforming tabular data with automatic Pandas code generation.
Jupyter notebooks implementing Gilbert Strang's MIT linear algebra course (18.06) with Python examples.
A guide to applying Test-Driven Development and clean architecture principles when building software from Jupyter notebooks.
A pytest plugin that validates Jupyter notebooks by executing cells and comparing outputs to ensure consistency.
Educational materials for the textbook 'A First Course in Network Science', including Python tutorials, datasets, and Jupyter notebooks.
Jupyter notebooks implementing algorithms, proofs, and summaries from 'The Elements of Statistical Learning' textbook.
A command-line test framework for Jupyter Notebooks that executes notebooks and runs unittest/doctest cells.
An interactive book about Riemann problems and approximate solvers for hyperbolic PDEs, implemented in Jupyter notebooks.
Manage Jupyter notebooks as plain Python code with embedded Markdown for better development workflows and version control.
A UC Berkeley course teaching urban data analysis, visualization, and mapping using Python and open-source tools for city planning.
Python code for teaching computational economics, implementing Solow, Ramsey, and Real Business Cycle models.
A Python package for exploring and analyzing data from your Home Assistant database.
A collection of Jupyter notebooks demonstrating TensorFlow Lite model quantization, conversion, and optimization techniques for deep neural networks.
A Python library for creating interactive self-assessment quizzes (multiple choice, numerical, string) in Jupyter notebooks and Jupyter Book.
A GitHub Action that automatically tests Jupyter notebooks from top to bottom using nbmake and pytest.
A PhD-level course on computational economics covering Python, Julia, and economic modeling techniques.
A curated collection of Jupyter notebooks for digital humanities research and teaching, covering text analysis, data visualization, and more.
A hands-on workshop introducing deep learning concepts with practical examples using neural networks, CNNs, RNNs, and autoencoders.
A desktop application for interactive computing with Jupyter notebooks, supporting multiple kernels and rich outputs.
A CLI tool to reduce git conflicts in Jupyter notebooks by clearing metadata and resolving merge conflicts.
A Python toolbox for analyzing multiplexed imaging data, featuring segmentation, pixel/cell clustering, and spatial analysis.
A JupyterLab extension for writing and running unit tests directly within Jupyter notebook cells.
A collection of Jupyter notebooks for learning the neon deep learning framework through hands-on tutorials.
Automatically saves and versions Jupyter notebooks on every save with persistent checkpoints in JupyterLab.
A collection of interactive Jupyter notebooks for learning Hadoop, Spark, and MapReduce with hands-on tutorials and demos.
A comprehensive Java library for statistics, data mining, and machine learning with interactive notebook support.
Interactive Jupyter notebook exercises for an audio signal processing lecture, covering practical implementations and simulations.
A collection of Jupyter notebooks for analyzing Common Crawl web archive data using columnar indexes and webgraph datasets.
A collection of quantitative trading research experiments exploring uncommon strategies and techniques through Jupyter notebooks.
Interactive 3D graphics in Jupyter notebooks using Python for scientific visualization and education.
Jupyter notebooks for hands-on Big Data Analytics exercise classes covering Spark ML, Map/Reduce algorithms, and deep learning.
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