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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.
A game theoretic approach to explain the output of any machine learning model.
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…
Data and code behind the articles and graphics at FiveThirtyEight
The "Python Machine Learning (1st edition)" book code repository and info resource
Automatic extraction of relevant features from time series:
Notebooks and code for the book "Introduction to Machine Learning with Python"
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.
Think DSP: Digital Signal Processing in Python, by Allen B. Downey.
The Jupyter Notebooks behind my OReilly report, "A Whirlwind Tour of Python"
An easy to use blogging platform, with enhanced support for Jupyter Notebooks.
A sequence of Jupyter notebooks featuring the "12 Steps to Navier-Stokes" http://lorenabarba.com/
Examples of matplotlib codes and plots
Some Python Implementations of the Kalman Filter
[DEPRECATED] See the new edition:
Classical Aerodynamics of potential flow using Python and Jupyter Notebooks
A course in numerical methods with Python for engineers and scientists: currently 5 learning modules, with student assignments.
An introduction to data science using Python and Pandas with Jupyter notebooks
Digital Signal Processing - Theory and Computational Examples
Tutorials and information on the Julia language for MIT numerical-computation courses.
Interactive Web Plotting with Bokeh in IPython notebook
Fastest-lap is a vehicle dynamics simulator. It can be used to understand vehicle dynamics, to learn about driving techniques, to design car prototypes, or just for fun!
A tutorial on Julia DataFrames package
Materials for MIT 6.S083 / 18.S190: Computational thinking with Julia + application to the COVID-19 pandemic
Notes on Scientific Computing for Biomechanics and Motor Control
Tutorial on geospatial data manipulation with Python
Scientific Python Geometric Algorithms Library