Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"
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Updated
Nov 12, 2022 - Jupyter Notebook
Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"
Lecture notes on Bayesian deep learning
Applied Probability Theory for Everyone
Explore "Statistics" and "Probability Theory" Concepts and Their Implementations in "Python"
Unofficial solutions for Introduction to Probability, Second Edition by Joseph Blitzstein and Jessica Hwang.
Rust for data analysis encyclopedia (WIP).
考研数学同济高等数学第七版线性代数浙大概率论
Implementation of domain-specific language (DSL) for dynamic probabilistic programming
A quick introduction to all most important concepts of Probability Theory, only freshman level of mathematics needed as prerequisite.
🚀 A library designed to facilitate work with probability, statistics and stochastic calculus
Mathematical preliminaries for machine learning
A Comprehensive AX = XB Calibration Solvers in Matlab
Probability Calculations with Random Numbers via Slot Machine Spin Simulation. Includes Probability Table and Results. Console App C#.
My solutions to Paul L. Meyer's "Introductory Probability and Statistical Applications, 2nd ed.", ISBN 0-201-04710-1.
The lecture notes for my discrete mathematics classes.
Applied Machine Learning Course
A courseware module that covers the fundamental concepts in probability theory and their implications in data science. Topics include probability, random variables, and Bayes' Theorem.
Introduction to R applied to statistics and econometrics
Materials for Probability Theory and Modelling
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