Alternatives and similar repositories for jupyter-notebook-extensions

Users that are interested in jupyter-notebook-extensions are comparing it to the libraries listed below

• WillKoehrsen / mastering-large-datasets
  Work for Mastering Large Datasets with Python
  ☆20Updated 3 years ago
• rasbt / DeepLearning-Gdansk2019-tutorial
  Ordinal Regression tutorial for the International Summer School on Deep Learning 2019
  ☆70Updated 6 years ago
• WillKoehrsen / fastai-numerical-linear-algebra
  ☆12Updated 7 years ago
• saturncloud / saturn-cloud-examples
  ☆19Updated 4 years ago
• brohrer / cottonwood
  A flexible neural network framework for running experiments and trying ideas.
  ☆82Updated 5 years ago
• amueller / talks_odt
  Slides and materials for most of my talks by year
  ☆92Updated 2 years ago
• WillKoehrsen / classic_computer_science
  Classic Computer Science Problems with Python
  ☆29Updated 6 years ago
• WillKoehrsen / data-science-for-good
  Data Science for Good Projects
  ☆50Updated 7 years ago
• d6t / d6tflow-template
  Project template for highly effective data science workflows
  ☆29Updated last month
• WillKoehrsen / probabilistic-programming
  Experimenting with and teaching probabilistic programming
  ☆105Updated 3 years ago
• jupyterlab / scipy2018-jupyterlab-tutorial
  Tutorial material and instruction for scipy 2018 jupyterlab tutorial
  ☆73Updated 3 months ago
• Quansight / scipy-2019-rapids-tutorial
  Materials for the SciPy 2019 RAPIDS tutorial
  ☆22Updated 6 years ago
• thomasjpfan / skconfig
  Experimental library for sampling and validating scikit-learn parameters
  ☆10Updated 6 years ago
• ritchieng / machine-learning-nanodegree
  Codes, notes and guides on Udacity's machine learning nanodegree.
  ☆81Updated 9 years ago
• dziganto / Data_Science_Fundamentals
  ☆101Updated 7 years ago
• navdeep-G / interpretable-ml
  Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
  ☆21Updated 3 years ago
• georgian-io-archive / automl_benchmark
  Distributed, large-scale, benchmarking framework for rigorous assessment of automatic machine learning repositories, projects, and librar…
  ☆30Updated 3 years ago
• vaexio / vaex-talks
  Talks about vaex
  ☆36Updated 3 years ago
• lopusz / pydata-berlin-2018
  ☆15Updated 7 years ago
• dipanjanS / art_of_data_visualization
  The art of effective visualization of multi-dimensional data
  ☆164Updated 7 years ago
• croach / statistics-for-hackers
  A Jupyter notebook to accompany Jake VanderPlas's "Statistics for Hackers" talk from PyCon 2016.
  ☆77Updated 7 years ago
• amueller / ml-workshop-short
  Two hour interactive machine learning workshop
  ☆22Updated 5 years ago
• DunderData / Tutorials
  Python data science and machine learning from Ted Petrou with Dunder Data
  ☆55Updated 3 years ago
• AllenDowney / BayesSeminar
  Bayesian statistics seminars
  ☆29Updated 8 years ago
• schmidtbri / simple-ml-model-abc
  Code demonstrating a simple Machine Learning model abstract base class and its uses.
  ☆14Updated 2 years ago
• Startupsci / data-science-notebooks
  Jupyter notebooks for learning Python and Data Science, companion to Data Science Solutions book.
  ☆37Updated 5 years ago
• kirit93 / NumpyTutorial
  Notebooks for tutorial on Numpy.
  ☆59Updated 5 years ago
• hugobowne / machine_learning_techniques
  In which I implement some applications of machine learning techniques.
  ☆31Updated 9 years ago
• tdpetrou / integrating-pandas-with-scikit-learn
  Tutorial covering a new workflow available going from pandas to scikit-learn
  ☆39Updated 3 years ago
• amueller / ml-training-advanced
  Materials for the "Advanced Scikit-learn" class in the afternoon
  ☆165Updated 7 years ago