How Bend Works: A Parallel Programming Language That "Feels Like Python but Scales Like CUDA&#
A brief introduction to Lambda Calculus, Interaction Combinators, and how they are used to parallelize operations on Bend / HVM.- 25960Murphy2025-03-22
Data Visualization Generation Using Large Language and Image Generation Models with LIDA
An overview of the LIDA library, including how to get started, examples, and considerations going forward- 26276Murphy2025-03-22
Estimate the unobserved – Moving-Average Model Estimation with Maximum Likelihood in Python
How unobserved covariates' coefficients can be estimated with MLE- 29128Murphy2025-03-22
PySpark Explained: Dealing with Invalid Records When Reading CSV and JSON Files
Effective techniques for identifying and handling data errors- 23565Murphy2025-03-22
A Complete Guide to Master Step Functions on AWS
Workflow orchestration made easier- 26249Murphy2025-03-22
Bayesian A/B Testing Falls Short
Over the past decade, I’ve engaged in countless discussions about Bayesian A/B testing versus Frequentist A/B testing. In nearly every conversation, I’ve maintained the same viewpoint: there’s a significant disconnect between the industry’s enthusia- 25590Murphy2025-03-22
3 Challenges to Being a Data Scientist in 2024
Given the current climate, is data science for you?- 27035Murphy2025-03-22
Mastering Object Counting in Videos
Step-by-step guide to counting strolling ants on a tree using detection and tracking techniques.- 26200Murphy2025-03-22
A New Method to Detect "Confabulations" Hallucinated by Large Language Models
By calculating semantic entropy with a second LLM, we can better flag answers as unreliable due to lack of knowledge- 21910Murphy2025-03-22
CRAG – Intuitively and Exhaustively Explained
Defining the Limits of Retrieval Augmented Generation- 29396Murphy2025-03-22
Making LLMs Write Better and Better Code for Self-Driving Using LangProp
Analogy from classical machine learning: LLM (Large Language Model) = optimizer; code = parameters; LangProp = PyTorch Lightning- 26202Murphy2025-03-22
Classification Loss Functions: Intuition and Applications
A simpler way to understand derivations of loss functions for classification and when/how to apply them in PyTorch- 21695Murphy2025-03-22
Improving RAG Performance Using Rerankers
A tutorial on using rerankers to improve your RAG pipeline- 23245Murphy2025-03-22
Prompt Engineering: Tips, Approaches, and Future Directions
Our weekly selection of must-read Editors' Picks and original features- 22729Murphy2025-03-22
System Design: Load Balancer
Orchestrating strategies for optimal workload distribution in microservice applications- 28967Murphy2025-03-22
Demonstrating Prioritization Effectiveness in Sales
The power of machine learning for contact priorization- 21561Murphy2025-03-22
The Intuitive Basics of Optimization
A gentle introduction to the amazing field of optimization- 27413Murphy2025-03-22
Understanding Transformers
A straightforward breakdown of "Attention is All You Need"¹- 20987Murphy2025-03-22
The Data Scientist's Guide to Choosing Data Vendors
A practical guide to effectively evaluating and deciding on data to enrich and improve your models- 24683Murphy2025-03-22
5 Habits That Made Me A Data Scientist
Advice and tips on becoming a data scientist- 25296Murphy2025-03-22
Genius Cliques: Mapping out the Nobel Network
Combining Network Science, Data Visualization, and Wikipedia to uncover hidden connections between all the Nobel laureates.
Data Science Expertise Comes in Many Shapes and Forms
Our weekly selection of must-read Editors' Picks and original features