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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…
PRML algorithms implemented in Python
Python code for "Probabilistic Machine learning" book by Kevin Murphy
A course in reinforcement learning in the wild
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
An interactive book on deep learning. Much easy, so MXNet. Wow. [Straight Dope is growing up] ---> Much of this content has been incorporated into the new Dive into Deep Learning Book available at …
A python tutorial on bayesian modeling techniques (PyMC3)
Notebooks for "Python for Signal Processing" book
"Deep Generative Modeling": Introductory Examples
Gaussian Process Optimization using GPy
Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"
Code accompanying the ECCV 2020 paper "Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data" by Tim Salzmann*, Boris Ivanovic*, Punarjay Chakravarty, and Marco Pavone (…
A collection of Bayesian data analysis recipes using PyMC3
PyTorch implementation of VQ-VAE by Aäron van den Oord et al.
Tutorial for using Kitti dataset easily
Code and assignment repository for the Imperial College Mathematics department Deep Learning course
Design your own matplotlib stylefile interactively
Deep learning course CE7454, 2019
Interactive textbook on state-space models
Learning human driver models from NGSIM data with imitation learning.
[Outdated. Please use https://github.com/numba/numba-examples] Examples of NumbaPro in use.
A system for scientific simulation-based inference at scale.
Python code for "Multivariable Feedback Control"
PyTorch implementation of "Conditional Image Generation with PixelCNN Decoders" by van den Oord et al. 2016
APPM 5630 at CU Boulder
Some teaching material and other educational resources