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.- 25917Murphy2025-03-22
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- 26234Murphy2025-03-22
Estimate the unobserved – Moving-Average Model Estimation with Maximum Likelihood in Python
How unobserved covariates' coefficients can be estimated with MLE- 29086Murphy2025-03-22
PySpark Explained: Dealing with Invalid Records When Reading CSV and JSON Files
Effective techniques for identifying and handling data errors- 23524Murphy2025-03-22
A Complete Guide to Master Step Functions on AWS
Workflow orchestration made easier- 26208Murphy2025-03-22
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- 25549Murphy2025-03-22
3 Challenges to Being a Data Scientist in 2024
Given the current climate, is data science for you?- 26995Murphy2025-03-22
Mastering Object Counting in Videos
Step-by-step guide to counting strolling ants on a tree using detection and tracking techniques.- 26160Murphy2025-03-22
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- 21869Murphy2025-03-22
CRAG – Intuitively and Exhaustively Explained
Defining the Limits of Retrieval Augmented Generation- 29356Murphy2025-03-22
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- 26160Murphy2025-03-22
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- 21654Murphy2025-03-22
Improving RAG Performance Using Rerankers
A tutorial on using rerankers to improve your RAG pipeline- 23204Murphy2025-03-22
Prompt Engineering: Tips, Approaches, and Future Directions
Our weekly selection of must-read Editors' Picks and original features- 22687Murphy2025-03-22
System Design: Load Balancer
Orchestrating strategies for optimal workload distribution in microservice applications- 28926Murphy2025-03-22
Demonstrating Prioritization Effectiveness in Sales
The power of machine learning for contact priorization- 21521Murphy2025-03-22
The Intuitive Basics of Optimization
A gentle introduction to the amazing field of optimization- 27372Murphy2025-03-22
Understanding Transformers
A straightforward breakdown of "Attention is All You Need"¹- 20946Murphy2025-03-22
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- 24641Murphy2025-03-22
5 Habits That Made Me A Data Scientist
Advice and tips on becoming a data scientist- 25255Murphy2025-03-22
The current state of continual learning in AI
Why is ChatGPT only trained up until 2021?
Optimizing Pandas Code: The Impact of Operation Sequence
Learn how to rearrange your code to achieve significant speed improvements.