- Math Fundamentals
- Mathematics for Machine Learning
- Topic-wise notes: maths & stats
- Number Representation Systems Explained in One Picture
- Abductive Learning: Towards Bridging Machine Learning and Logical Reasoning: Slides | Video | GitHub
- A Simple Introduction to Complex Stochastic Processes
- Quick math references
- Mathematics for Machine Learning
- Patrick Landreman: A Crash Course in Applied Linear Algebra | PyData New York 2019
- 👏Linear Algebra👏 by Jim Hefferon
- #Tensor #Calculus for Deep learning which is used in Google #Tensorflow. Designed by Prof. Dr. Cornelis P. Dullemond
- Mathematical Understanding of CNN: course notes of Andrew Ng
- ”A Beginner's Guide to the Mathematics of Neural Networks” LinkedIn Post
- [Visual proof of the so-called Nicomachus theorem](https://media-exp1.licdn.com/dms/image/C4E22AQHlY8BHlHAA5g/feedshare-shrink_1280/0?e=1589414400&v=beta&t=NyBaXMFDQcf21eZQWOlBeMPXo1MWbC0ejv5Q2DMkj44 Wikipedia LinkedIn Post (LinkedIn Sign-in required)
- This simple introduction to #matrix theory offers a refreshing perspective on the subject LinkedIn Post
- ✅Mathematics for Data Science✅: 3Blue1Brown | Khan Academy: Math | Krista King's classes LinkedIn Post
- Mathematics for Machine Learning
- Explaining the maths of #backpropagation is hard and there are many good resources covering the maths of #deeplearning LinkedIn Post
- Math model: simulating an epidemic
- 🐍 Linear Algebra in Python 🐍 LinkedIn Post
- Neural-Symbolic Learning and Reasoning 📝 LinkedIn Post
- Mathematics is important for #machinelearning, #datascience, #artificialintelligence
- The Math of Random Forests and Feature Importance in Scikit-learn and Spark
- Let's Learn Basic Mathematics - Sigma Notations
- Why Study Linear Algebra?😉
- Statistics formula for Data Science
- Statistics Fundamentals
- Statistics for Machine learning (paid book: Packt Publishing)
- Topic-wise notes: maths & stats
- 5 Lesson 5 Measures Of Skewness And Kurtosis [deadlink]
- Data Types in Statistics
- An Introduction To Statistical Learning with Applications in R
- Fractional Exponentials - Dataset to Benchmark Statistical Tests
- Amortized Monte Carlo Integration by Tom Rainforth
- Kernel Embeddings, Meta Learning & Distributional Transfer by Dino Sejdinovic
- Thomas Wiecki’s presentation: Machine Learning and Statistics - don't mind the gap | Thomas Wiecki
- Interactive Machine Learning, Deep Learning and Statistics websites
- G. James, D. Witten et al., An Introduction to Statistical Learning with Applications in R
- Static and dynamic network visualization with R - Katya Ognyanova
- Learning from Data: the art of statistics | The Art of Statistics: Learning from Data by David Spiegelhalter
- Statistical Rethinking [deadlink]
- Abductive Learning: Towards Bridging Machine Learning and Logical Reasoning: Slides | Video | GitHub
- 32 Type of Statisical Distribution, by Rasmus Baath
- Book: Statistics for Non-Statisticians
- 👉 Statistics Quick Reference 👈
- A Semi-Supervised Classification Algorithm using Markov Chain and Random Walk in R
- Interesting Problem: Self-correcting Random Walks
- 24 Uses of Statistical Modeling: Part I | Part II
- Statistical Models (detailed diagram)
- Encyclopedia of Statistics by Data Science Central
- Statistical Inquiry Cycle
- Your Guide to Master Hypothesis Testing in Statistics
- 🎯 Most #important #statistics concept for a #Datascientist in one #image and its an extension
- Basic Understanding 🤔 of Statistics🕸️ Notes 📔 Best Statistics courses on Internet
- Statistics Cheatsheet
- Didn't Learn Statistics Yet?
- 7 Traps to Avoid Being Fooled by Statistical Randomness
- Three classes of metrics: centrality, volatility, and bumpiness
- Why Including Effect Size and Knowing your Statistical Power ~ are Important
- Important #Statistics formula in one picture...👈👈👈Must reas
- The Cartoon Guide To Statistics
- 9 Off-the-beaten-path Statistical Science Topics with Interesting Applications
- Here are some essential math/stats for #DataScience
- Stats + Data Science Education
- Statistics for Data Science in One Picture
- Diff between stats and DS: big data and inferential stats
- Data Science: The End of Statistics?
- Statistical Significance Tests for Comparing Machine Learning Algorithms
- Statistical Modeling; Selecting Predictors is a Challenge for Data Scientists
- Machine Learning vs. Traditional Statistics: Different philosophies, Different Approaches
- Becoming a Master of Statistical Inference by Robert Wood
- Ten Simple Rules for Effective Statistical Practice
- 💦 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗶𝘀 𝗮𝗻 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗽𝗮𝗿𝘁 𝗳𝗼𝗿 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮, 𝘀𝗼 𝗯𝗲𝗳𝗼𝗿𝗲 𝗱𝗲𝗲𝗽 𝗱𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝘁𝘀 𝗴𝗼𝗼𝗱 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝗼𝗳 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝘄𝗵𝗲𝗿𝗲 𝗶𝘁'𝘀 𝘂𝘀𝗲𝗱.
- Practical Statistics
- Deciphering information and misinformation: Inspired by the book "A Field Guide to Lies and Statistics"
- The 17 equations that changed the course of history
- Statistics by Chris Albon - covering Frequentist topics
- See Data > Statistics section more related links
- Probabilistic Symmetry and Invariant Neural Networks by Benjamin Bloem-Reddy
- Practical Probabilistic Programming book (pdf)
- Suite of probabilitic programming language repos from Improbable.io
- Chris Fonnesbeck’s presentation: PyMC's Big Adventure - Lessons Learned from the Development of Open-source Software for Probabilistic Programming | Chris Fonnesbeck
- Amortized Monte Carlo Integration by Tom Rainforth
- Books
- Think Stats, 2nd edition | github - is an introduction to Probability and Statistics for Python programmers
- Learning & Reasoning in Artificial Intelligence by Thomas Lukasiewicz
- Abductive Learning: Towards Bridging Machine Learning and Logical Reasoning: Slides | Video | GitHub
- Random Number Generation and Sampling Methods
- 👉 Introduction to #probability 👈
- #Probability #understanding in one #image
- Understanding the applications of Probability in Machine Learning
- How to approach Hypothesis Testing
- Probability for Machine Learning
- Different Probability Distributions in One Picture
- Probability for Machine Learning
- How to calculate covariance with probability distribution in R?
- Data Scientists Must Know Probability
- Generating Pareto Distribution in Python
- Capsule Networks -- A Probabilistic Perspective
- Pomegranate: PyPi | docs | GitHub
- Data Science in Courses
- Bayesian active learning with Gaussian processes | source code | John Reid
- Probabilistic Programming & Bayesian Methods for Hackers - Cam Davidson-Pilon
- Bayesian nonparametric ML through randomized loss functions & posterior bootstraps by Chris Holmes
- A visual guide to Bayesian thinking
- Visualization in Bayesian workflow
- Skillsmatter: Precision Medicine With Mechanistic, Bayesian Models [deadlink]
- Colin Carroll’s presentation: Tidy and beautiful - Visualizing Bayesian models with xarray and ArviZ | Colin Carroll
- Books
- Probability Learning I : Bayes’ Theorem
- Probability Learning II: How Bayes’ Theorem is applied in Machine Learning
- Bayesian Framework
- Implementing the Bayesian Framework
- Bayesian Machine Learning
- A "quick" introduction to PyMC3 and Bayesian models
- Some Applications of Markov Chain in Python
- A curated list of resources dedicated to bayesian deep learning
- Analysis of Perishable Products Sales Using Bayesian Inference
- Naive Bayes for Dummies; A Simple Explanation
- What is Baysean Linear Regressions (Part 1)
- 3 Ways to Think about Bayes’ Rule by Isaac Riley
- How to Implement Bayesian Optimization from Scratch in Python
- Bayesian Methods and Networks in classical and quantum physics
- An introduction to (and puns on) Bayesian neural networks
- Bayesian Inference Algorithms: MCMC and VI
- A chat w/ @twiecki about #BayesianHierarchicalModels! What are they? How to build them? And why you should be very careful when building them
- Bayesian Hyperparameter Optimization - A Primer: Blog | Notebook
- How to Implement Bayesian Optimization from Scratch in Python
- Develop an Intuition for Bayes Theorem With Worked Examples
- Bayesian Stats 101 for Data Scientists
- New Marketing Insight from Unsupervised Bayesian Belief Networks
- Everything you need to know about Gaussian Distribution
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