The Assumptions of Logistic Regression (Explained in Plain English)
Learn the six assumptions of logistic regression, what violations look like, and how to fix them.
machine-learning
Bits & Boxes: Where Intelligent Software Meets Physical Fulfillment: The Orchestration Gap
Backpropagation Explained for Beginners (Part 3): How Backpropagation Really Works
From one gradient to every gradient
Introduction to Semi-Supervised Learning | Towards Data Science
A primer about Semi-Supervised Learning, the approaches taken with different algorithms and the limitations of using unlabelled data.
Understanding Cross-Entropy Loss: The Training Signal Behind Perplexity
Learn how cross-entropy loss turns next-word prediction into the training signal behind perplexity.
Polynomial Regression: How to Fit and Interpret Curved Relationships
Learn what polynomial regression is, when to use it, and how to interpret curved model results correctly.
Meet Flash-KMeans: An IO-Aware, Exact K-Means That Runs Over 200× Faster Than FAISS on GPUs - MarkTechPost
Flash-KMeans is an IO-aware, exact K-Means that uses FlashAssign and Sort-Inverse Update to outperform FAISS by over 200×.
MLE (max likelihood estimation) (@probabilityandstatistics)
Maximum Likelihood Estimation is the quiet engine underneath almost everything in ML, DL, and RL. Most people learn it once in a stats class and move on. They shouldn't. It's not just a parameter-fitting trick — it's the philosophical foundation of why our models work at all…
How to Estimate the Average Treatment Effect (ATE) with DoWhy How to Estimate the Average Treatment Effect (ATE) with DoWhy
Estimate the Average Treatment Effect in Python using DoWhy with regression and propensity score matching on real data.
Getting Started with DoWhy, the Python Causal Inference Library
Learn how to build and test causal inference models in Python using the DoWhy library.
Why Gradient Descent Zigzags and How Momentum Fixes It - MarkTechPost
How momentum optimizes gradient descent by dampening oscillations and accelerating convergence on complex, anisotropic neural network loss surfaces
Sigmoid vs ReLU Activation Functions: The Inference Cost of Losing Geometric Context - MarkTechPost
Explore Sigmoid vs ReLU activation functions and how losing geometric context affects neural network efficiency and depth.
AI for American-Produced Cement and Concrete - Engineering at Meta
Meta is continuing its long-term roadmap to help the construction industry leverage AI to produce high-quality and more sustainable concrete mixes, as well as those exclusively produced in the Unit…
Finding X-Risks and S-Risks by Gradient Descent — LessWrong
(This is a fleshed out version of the lightning talk I gave at Megameetup) …
The Concise Guide to Regularization: L1, L2, and Elastic Net The Concise Guide to Regularization: L1, L2, and Elastic Net
Learn when to use L1, L2, and Elastic Net regularization to prevent overfitting in machine learning models. Learn L1, L2, and Elastic Net regularization in simple terms. Prevent overfitting and build better machine learning models that generalize well.
Probabilistic Nearest Neighbor Search: The Swiss Army Knife of GenAI
ANN — Approximate Nearest Neighbors — is at the core of fast vector search, itself central to GenAI, especially GPT and LLM. My new methodology, abbreviated as PANN, has many other app…
5 Statistical Methods That Dominate Their Industries (and Why)
Discover which statistical methods dominate healthcare, finance, tech, retail, and sports analytics, and why these industry-method pairings became indispensable standards.
Shapley Values Explained: Seeing Which Features Drive Your Predictions
Learn what Shapley values are and how SHAP tools help explain machine learning predictions.
Kernel Principal Component Analysis (PCA): Explained with an Example - MarkTechPost
Dive into Kernel PCA: explained with an example demonstrating its effectiveness compared to traditional PCA for nonlinear data.
What exactly does word2vec learn?
The BAIR Blog
Weights & Biases: A KDnuggets Crash Course - KDnuggets
A hands-on guide to tracking experiments, versioning models, and keeping your ML projects reproducible with Weights & Biases.
9 machine learning concepts for ML engineers!
9 machine learning concepts for ML engineers! (explained as visually as possible) Here's a recap of several visual summaries posted in the Daily Dose of Data Science newsletter. 1️⃣ 4 strategies for Multi-GPU Training. - Training at scale? Learn these strategies to maximize…
Carl-McBride-Ellis/Compendium-of-free-ML-reading-resources: Compendium of free ML reading resources
Compendium of free ML reading resources
10 Must-Know Python Libraries for MLOps in 2025
In this article, we’ll explore 10 Python libraries that every machine learning professional should know in 2025.
Note: 1. MLflow tracks ML experiments and models. It makes it easy to compare results and share models with your team. Experiment Tracking: Track and compare multiple runs of your machine learning experiments. Model Packaging: Package code in a standard format using a MLproject file. Model Registry: A centralized store for managing lifecycle stages of models. 2. Data Version Control (DVC) lets you…
The Hundred-Page Machine Learning Book by Andriy Burkov
All you need to know about Machine Learning in a hundred pages. Supervised and unsupervised learning, support vector machines, neural networks, ensemble methods, gradient descent, cluster analysis and dimensionality reduction, autoencoders and transfer learning, feature…
Simplest backpropagation explainer without chain rule
Neural Networks learn to predict by backpropagation. This article aims to help you, build a solid intuition about the concept using a simple example. The ideas we learn here can be expanded for bigger nerual network. I assume that you already know how feed forward neural network…
“Periodic table of machine learning” could fuel AI discovery
After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, MIT researchers organized them into a “periodic table of machine learning” that can help scientists combine elements of different methods to improve algorithms or create new ones.
Cross-entropy and KL divergence - Eli Bendersky's website
Machine Learning Algorithms You Never Knew Existed, But Are Quite Useful
Ever heard of Tsetlin Machines ??
Beginner’s Guide to Deploying a Machine Learning API with FastAPI
In this guide, you will learn how to deploy a machine learning model as an API using FastAPI. We will create an API that predicts the species of a penguin based on its bill length and flipper length. Prerequisites Step 1: Set Up Your Environment Step 2: Prepare Your Machine…
How to Implement CatBoost in R
This article shows how to implement CatBoost in R.
What is triplet loss? - Dataconomy
Triplet loss is a machine learning function that minimizes distances between similar data points while maximizing distances between dissimilar ones.
What are Support Vector Machines (SVM)? - Dataconomy
Support Vector Machines (SVM) are supervised learning algorithms used for classification and regression by finding optimal decision boundaries between data classes.
Olivier Grisel - Predictive survival analysis with scikit-learn, scikit-survival and lifelines
10 Little-Known Python Libraries That Will Make You Feel Like a Data Wizard - KDnuggets
In this article, I will introduce you to 10 little-known Python libraries every data scientist should know.
50+ Projects to Learn Data Analysis | Aman Kharwal
In this article, I'll take you through a list of 50+ Data Analysis Projects you should try to learn Data Analysis.
A Practical Guide to Survival Analysis
Survival analysis consists of statistical methods that help us understand and predict how long it takes for an event to occur.
Support Vector Machines: A Progression of Algorithms
MMC, SVC, SVM: What’s the difference?
80+ Data Science Projects | Aman Kharwal
In this article, I'll take you through a list of 80+ hands-on Data Science projects you should try to learn everything in Data Science.
50+ AI & ML Projects with Python | Aman Kharwal
In this article, I'll take you through a list of 50+ AI & ML projects solved & explained with Python that you should try.
Implementing A Byte Pair Encoding (BPE) Tokenizer From Scratch
This is a standalone notebook implementing the popular byte pair encoding (BPE) tokenization algorithm, which is used in models like GPT-2 to GPT-4, Llama 3,...
Don't use cosine similarity carelessly - Piotr Migdał
Cosine similarity - the duct tape of AI. Convenient but often misused. Let's find out how to use it better.
Massively Speed-Up your Learning Algorithm, with Stochastic Thinning - Machine Learning Techniques
Dramatically Speed-Up your Learning Algorithm, with Stochastic Thinning. Includes use case, Python code, regression and neural network illustrations.
7 Essential Python Libraries for MLOps - KDnuggets
Popular MLOps Python tools that will make machine learning model deployment a piece of cake.
Note: 1. MLflow MLflow is an open-source framework that manages the complete machine learning lifecycle, from experimentation to deployment. It offers four key components: Tracking: Log parameters, metrics, and artifacts from your machine learning model runs. Projects: Package data science code in a reusable, reproducible form. Models: Deploy models from any ML library in a variety of ways. Registry…
10 Types of Machine learning Algorithms and Their Use Cases
In today’s world, you’ve probably heard the term “Machine Learning” more than once. It’s a big topic, and if you’re new to it, all the technical words might feel confusing. Let’s start with the basics and make it easy to understand. Machine Learning, a subset of Artificial…
How to Run a Paper Club (also: LIVE at NeurIPS 2024!)
Your ultimate Paper Club Starter Kit, from your friends at the Latent Space Paper Club, where we have now read 100 papers. Also: Announcing Latent Space Paper Club LIVE! at Neurips 2024! Join us!
AI Alone Isn’t Ready for Chip Design
A combination of classical search and machine learning may be the way forward
Transforming Location Retrieval at Airbnb: A Journey from Heuristics to Reinforcement Learning
How Airbnb leverages machine learning and reinforcement learning techniques to solve a unique information retrieval task in order to…
Difference Between a Batch and an Epoch in a Neural Network - MachineLearningMastery.com
Stochastic gradient descent is a learning algorithm that has a number of hyperparameters. Two hyperparameters that often confuse beginners are the batch size and number of epochs. They are both integer values and seem to do the same thing. In this post, you will discover the…
Calculating the Uncertainty Coefficient (Theil’s U) in Python
A measure of correlation between discrete (categorical) variables