Guest Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more Quantum computing (QC) brings with it a mix of groundbreaking possibilities and significant risks. Major tech…
Merkle Trees in the real world
A visual, interactive guide to what bloom filters are, when you would use them, and how they work.
Ruby implementation of Algorithms,Data-structures and programming challenges - kumar91gopi/Algorithms-and-Data-Structures-in-Ruby
Evolutionary algorithms are optimization techniques inspired by natural evolution, solving complex problems through processes like selection, adaptation, and mutation.
In 1993, Intel released the high-performance Pentium processor, the start of the long-running Pentium line. I've been examining the Pentium'...
"A calculator app? Anyone could make that." (this was originally a https://x.com/ChadNauseam/status/1890889465322786878) Not true. A calculator should show you the result of the mathematical expressi…
"A calculator app? Anyone could make that."
A young computer scientist and two colleagues show that searches within data structures called hash tables can be much faster than previously deemed possible.
How a Key-Value (KV) cache reduces Transformer inference time by trading memory for computation
To celebrate Rafael Araujo's talent and vision, I have curated a selection of his most stunning illustrations, showcasing the elegance and intricacy of his work.
Cosine similarity - the duct tape of AI. Convenient but often misused. Let's find out how to use it better.
Intel released the powerful Pentium processor in 1993, establishing a long-running brand of high-performance processors. 1 The Pentium incl...
Note: Based on Ken Shirriff's article "[Pi in the Pentium: reverse-engineering the constants in its floating-point unit](https://www.righto.com/2025/01/pentium-floating-point-ROM.html)", here is an outline of each section with explanations of the findings: | Section | Findings | |---|---| | **Introduction** | The Pentium processor's floating-point unit (FPU) computes transcendental functions like sine…
Dramatically Speed-Up your Learning Algorithm, with Stochastic Thinning. Includes use case, Python code, regression and neural network illustrations.
BM25 is a widely used algorithm for full text search. I wanted to understand how it works, so here is my attempt at understanding by re-explaining.
can I compare the BM25 scores of documents across multiple queries to determine which query the document best matches?
goal of a full text search algorithm is to take a query and find the most relevant documents
Support Vector Machines (SVMs) are a powerful and versatile supervised machine learning algorithm primarily used for classification and regression tasks. They excel in high-dimensional spaces and are particularly effective when dealing with complex datasets. The core principle…
A pseudorandom number generator may have good statistical properties but be insecure. But a physical RNG with good statistical properties is probably secure.
When their pay suddenly dropped, delivery drivers audited their employer
How phrase search works in search array by intersecting roaring-like numpy arrays.
All that lexical search context you need to build that RAG app
Understanding what genetic algorithms are and how they can be implemented in Python.
By Christos Kasparis, EnSilica EETimes (May 15, 2024) The rise of quanum computing paints a significant challenge for the cryptography we rely on today. The modern encryption standards we currently use to safeguard sensitive data and communications, such as DSA, public key RSA…
papad Hard to believe Sanjeev Arora and his coauthors consider it “a basic tool [that should be] taught to all algorithms students together with divide-and-conquer, dynamic programming, and random sampling.” Christos Papadimitriou calls it “so hard to believe that it has been…
Current encryption methods will likely be broken by 2030. Embedded systems in development now will have a reasonable chance of encountering attacks.
an open source textbook and reference work on algebraic geometry
we help students master the Kalman Filter
More and more industries are adopting “dynamic pricing” — and consumers aren’t happy.
What’s easy for a computer to do, and what’s almost impossible? Those questions form the core of computational complexity. We present a map of the landscape.
1. Preface Garbage Collection (GC) is an important feature of automatic memory management...
A Schur decomposition of a matrix $latex A\in\mathbb{C}^{n\times n}$ is a factorization $LATEX A = QTQ^*$, where $LATEX Q$ is unitary and $LATEX T$ is upper triangular. The diagonal entries of $LAT…
Tired of equations and matrices? Ready to learn the easy way? This post explains the Kalman Filter simply with pictures and examples!
Many Data Scientists overuse ML and neglect Mathematical Optimisation, even though it’s great for your career and easy to learn
Insanely fast and reliable smoothing and interpolation with the Whittaker-Eilers method.
In this article, I'll take you through the task of Market Basket Analysis using Python. Market Basket Analysis using Python.
Notebooks for the python tutorials of my youtube channel. See specific youtube video for link to specifc notebook. - lukepolson/youtube_channel
Definitions of algorithms, data structures, and classical Computer Science problems. Some entries have links to implementations and more information.
Research shows knowledge graphs and graph embeddings improve the quality and accuracy of Gen AI responses. Here’s how to get started.
Open Source resource for learning Data Structures & Algorithms and their implementation in any Programming Language
Read my manifesto on Code as an alternative to Mathematics. Code for this article can be found on this Colab Notebook should you choose to follow along. Why Kalman Filters? Kalman filters are ingenius. If you have never heard of them, then a very intuitive (and arguably…
A case study with step-by-step code implementation
Hierarchical Navigable Small World (HNSW) is a state-of-the-art algorithm used for an approximate search of nearest neighbours. Under the…
In the first two parts of this series we have discussed two fundamental algorithms in information retrieval: inverted file index and…
Hierarchical Navigable Small World graphs (HNSW) is an algorithm that allows for efficient nearest neighbor search, and the Sentence…
Similarity search is a popular problem where given a query Q we need to find the most similar documents to it among all the documents D.