Latent Variables & Expectation Maximization Algorithm

Bayesian Approach to Machine Learning

Genetic Algorithms in Python using the DEAP library

Applied to the optimization of a meal plan for macronutrients

How to Use Polynomial Feature Transforms for Machine Learning

Often, the input features for a predictive modeling task interact in unexpected and often nonlinear ways. These interactions can be identified and modeled by a learning algorithm. Another approach is to engineer new features that expose these interactions and see if they improve…

Speeding training of decision trees

New method reduces training time by up to 99%, with no loss in accuracy.

Looking Inside Mahalanobis Metric Matching

How does it actually work?

Understanding Associative Embedding

An elegant method to group predictions without labeling

Using Q-Learning in Numpy to teach an agent to play a game

Using q-learning for sequential decision making and therefore learning to play a simple game.

7 advanced tricks in pandas for data science

Pandas is the go-to library for data science. These are the shortcuts I use to do repetitive data science tasks faster and simpler.

A Simple Kalman Filter Implementation

A tank level measurement application to help you develop intuition on Kalman Filters

The Recursive Approximation Algorithm, Animated

How n-body problems are solved in linear time, without any maths.

Layered Label Propagation Algorithm

An algorithm for community finding

A Complete Beginners Guide to Matrix Multiplication for Data Science with P

Learn matrix multiplication for machine learning by following along with Python examples

Partial Correlation Vs. Conditional Mutual Information

Finding relationships between different variables/ features in a dataset during a data analysis task is one of the key and fundemental…

Co-variance: An intuitive explanation!

A comprehensive but simple guide which focus more on the idea behind the formula rather than the math itself — start building the block…

Matthews Correlation Coefficient: when to use it and when to avoid it

It’s not a silver bullet metric to classification problems

t-SNE clearly explained

An intuitive explanation of t-SNE algorithm and why it’s so useful in practice.

Matrix Factorization as a Recommender System

An Explanation and Implementation of Matrix Factorization

Xor Filters: Faster and Smaller Than Bloom and Cuckoo Filters: ACM Journal of Experimental Algorithmics: Vol 25, No null

The Bloom filter provides fast approximate set membership while using little memory. Engineers often use these filters to avoid slow operations such as disk or network accesses. As an alternative, a cuckoo filter may need less space than a Bloom filter ...

Local Links Run The World

Networks regulate everything from ant colonies and middle schools to epidemics and the internet. Here’s how they work.

A Mathematical Primer of Compression

How to reduce storage size without losing information.

DEPSO Algorithm: Project Portal

[bibshow file=own_pub.bib] DEPSO [bibcite key=Zhang:2003p3816], or called DEPS, is an algorithm for (constrained) numerical optimization problem (NOP). DEPSO combines the advantages of Particle Swarm Optimization (PSO) and Differential Evolution (DE). It is incorporated into…

Hyper-Parameter Optimization: A Review of Algorithms and Applications

Since deep neural networks were developed, they have made huge contributions to everyday lives. Machine learning provides more rational advice than humans are capable of in almost every aspect of...

Introduction to Evolutionary Algorithms

A practical introduction on how to program Evolutionary Algorithms in Python to solve optimization tasks.

How the Mathematical Conundrum Called the 'Knapsack Problem' Is All Around Us

A litany of issues in business, finance, container ship loading and aircraft loading derive from this one simple dilemma

BCrypt Explained

Intro If you're into Cryptography For Beginners, you're in the right place. Maybe you're j...

A better adaptive Runge-Kutta method

The Dormand-Prince algorithm is similar to the popular RKF45 adaptive Runge-Kutta method but produces more precision relative to the amount of computation.

ctgk/PRML: PRML algorithms implemented in Python

PRML algorithms implemented in Python.

Explorable Explanations

a hub for learning through play

Using Bayesian Kalman Filter to predict positions of moving particles / obj

In this article, we shall see how the Bayesian Kalman Filter can be used to predict positions of some moving particles / objects in 2D. This article is inspired by a programming assignment from the coursera course Robotics Learning by University of Pennsylvania, where the goal…

Interactive Lorenz Attractor

Interactive Lorenz attractor with 10,000 butterflies under the Lorenz attractor flow.

Kahan Summation Algorithm

In numerical analysis, the Kahan summation algorithm, also known as compensated summation,[1] significantly reduces the numerical error in the total obtained by adding a sequence of finite-precision floating-point numbers, compared to the obvious approach. This is done…

The BLAKE3 cryptographic hash function

the official Rust and C implementations of the BLAKE3 cryptographic hash function - BLAKE3-team/BLAKE3

What are some fast similarity search algorithms and data structures for hig

[R] Geometrical Regret Matching

2.9M subscribers in the MachineLearning community. Beginners -> /r/mlquestions , AGI -> /r/singularity, career advices -> /r/cscareerquestions…

Copulas Vs. Correlation

Copulas and Rank Order Correlation are two ways to model and/or explain the dependence between 2 or more variables. Historically used in biology and epidemiology, copulas have gained...

RSA Algorithm

A Self-Contained Tutorial on RSA Algorithm Theories for Number Theory Noobs

Finite Element Method

The finite element method (FEM) is a popular method for numerically solving differential equations arising in engineering and mathematical modeling. Typical problem areas of interest include the traditional fields of structural analysis, heat transfer, fluid flow, mass…

New Cryptography Method Promising Perfect Secrecy Is Met With Skepticism

An international team claims its perfect secrecy cryptography is made unbreakable by physics

teivah/algodeck: Algo Deck is an Open-Source Collection of 200 Algorithmic

An Open-Source Collection of 200+ Flash Cards to Help You Preparing Your Algorithms & Data Structures Interview 💯 - teivah/algodeck

Attracted to Attractors

Visualizing three, 3-dimensional strange attractors with a fractal structure as well as the differential equations that generate them.

[2001.03147] Age-Partitioned Bloom Filters

Bloom filters (BF) are widely used for approximate membership queries over a set of elements. BF variants allow removals, sets of unbounded size or querying a sliding window over an unbounded...

ritchieng/the-incredible-pytorch: The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to…

The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch. - GitHub - ritchieng/the-incredible-pytorch: The Incredible PyTorch: a curated list...

Xor Filters: Faster and Smaller Than Bloom Filters

Optimizers Explained - Adam, Momentum and Stochastic Gradient Descent

What does the Laplace Transform tell us? A visual explanation [video]

Sign up with brilliant and get 20% off your annual subscription: https://brilliant.org/MajorPrep/ STEMerch Store: https://stemerch.com/ Support the Channel: https://www.patreon.com/zachstar PayPal(one time donation): https://www.paypal.me/ZachStarYT This video goes through a…

Basic Linear Algebra for Deep Learning | Built In

Basic linear algebra can have a surprising influence on deep learning and machine learning. Learn what linear algebra is and how to use it.

On Sharding

The matrix calculus you need for deep learning

Most of us last saw calculus in school, but derivatives are a critical part of machine learning, particularly deep neural networks, which are trained by optimizing a loss function. This article is an attempt to explain all the matrix calculus you need in order to understand the…

A new way to make quadratic equations easy

Many former algebra students have painful memories of struggling to memorize the quadratic formula. A new way to derive it, overlooked for 4,000 years, is so simple it eliminates the need.

Hacker Laws: Laws, Theories, Principles, Patterns Developers Might Find Use

💻📖 Laws, Theories, Principles and Patterns that developers will find useful. #hackerlaws - dwmkerr/hacker-laws