Data Engineering: A Formula 1-inspired Guide for Beginners
A Glossary with Use Cases for First-Timers in Data Engineering- 22060Murphy2025-03-22
Version Controlling in Practice: Data, ML Model, and Code
A Step-by-Step Guide to Versioning in MLOps- 28630Murphy2025-03-22
Retrieval Augmented Generation (RAG) Inference Engines with LangChain on CPUs
Exploring scale, fidelity, and latency in AI applications with RAG- 24950Murphy2025-03-22
GPT – Intuitively and Exhaustively Explained
Exploring the architecture of OpenAI's Generative Pre-trained Transformers.- 23072Murphy2025-03-22
3 Powerful Python Libraries to (Partially) Automate EDA And Get You Started With Your Data Project
All machine learning problems are data problems. So, it makes sense that you should spend time understanding and cleaning your data- 21003Murphy2025-03-22
Large Models Meet Big Data: Spark and LLMs in Harmony
A step-by-step guide to use Apache Spark and large language models- 29949Murphy2025-03-22
On Why Machines Can Think
How can we think about thinking in the simplest way possible?- 26786Murphy2025-03-22
Enhancing Python Documentation: A Step-by-Step Guide to Linking Source Code
Bridging the Gap Between Documentation and Code: Simplifying Python Learning- 28279Murphy2025-03-22
DL Notes: Advanced Gradient Descent
The main optimization algorithms used for training neural networks, dissected and implemented from scratch in Python- 24603Murphy2025-03-22
Design Patterns with Python for Machine Learning Engineers: Prototype
Learn how to use the Prototype design pattern to enhance your code- 23467Murphy2025-03-22
Why Understanding the Data-Generation Process Is More Important Than the Data Itself
"The Book of Why" Chapters 5&6, a Read with Me series- 23787Murphy2025-03-22
LLMs for Everyone: Running LangChain and a MistralAI 7B Model in Google Colab
Experimenting with Large Language Models for free- 29664Murphy2025-03-22
Customize Colormaps with Matplotlib
Match your colors to your theme- 27372Murphy2025-03-22
The Principled Approach to Early Ranking Stages
It is well known that in recommendation systems, there are several stages of building recommendations: first comes candidate generation, also often referred to as retrieval, followed by one or more stages of ranking. Academic papers do not pay much attent- 20215Murphy2025-03-22
Web Speech API: What Works, What Doesn't, and How to Improve It by Linking It to a GPT Languag
Part of a series on how modern AI and other technologies could assist more efficient human-computer interactions- 23438Murphy2025-03-22
Object Detection using RetinaNet and KerasCV
Object detection using the power and simplicity of the KerasCV library.- 24634Murphy2025-03-22
Understanding Independence and Why it is Critical in Causal Inference and Causal Validation
Photo by Towfiqu barbhuiya on Unsplash Background In a recent article the author explored and explained how the concept of dependence can be used to validate a proposed Directed Acyclic Graph (DAG) against a dataset to identify spurious edges in the graph- 20550Murphy2025-03-22
Use the Partitions, Luke! A Simple and Proven Way to Optimise Your SQL Queries
If you've ever written an SQL query that takes ages to run, this is the article for you- 24885Murphy2025-03-22
Calling All Functions
Benchmarking OpenAI function calling and explanations- 24303Murphy2025-03-22
Time Series Classification for Fatigue Detection in Runners – A Tutorial
A step-by-step walk-through of inter-participant and intra-participant classification performed on wearable sensor data of runners- 26315Murphy2025-03-22
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.