In a beginner-friendly primer, Zubair Hossian unpacks three essential concepts that will help you understand how probability distributions work. buff.ly/4bDqL5U

For a deeper understanding of the inner mathematical workings of machine learning models, follow along Conor O'Sullivan's accessible explainer on Friedman's H-statistic. buff.ly/3V8nTXY

It's always a good idea to keep a close eye on your code's memory costs. Christopher Tao walks us through several effective approaches to measuring the memory consumption of a variable or function in Python. buff.ly/3VmTzdu

Unpacking the benefits of monosemanticity, Jack Chih-Hsu Lin explores recent research that points towards a future where activation engineering replaces prompt engineering as the dominant mode of interaction with LLMs. buff.ly/3V3Ge8a

Learn how you can simplify Streamlit app code by moving longer static contents to markdown files — Marcin Kozak's clear and concise tutorial breaks down the process step by step. buff.ly/4bX4q2S

'The transformer-based model I will fine-tune here is more than 1000 times smaller than GPT-3.5 Turbo. It will perform consistently better for this use case because it will be specifically trained for it.'

Ida Silfverskiöld expands in a new deep dive. buff.ly/3Rs5uEv

For a comprehensive and accessible introduction to the broader world of LLM domain adaptation, don't miss Aris Tsakpinis's new series: the first part lays the groundwork, defines key concepts, and unpacks the tradeoffs inherent to different approaches. buff.ly/3Vi8XaJ

Curious about building LLM-based applications? Don't miss Volker Janz's patient tutorial, which explains how you can create an AI-driven movie quiz by bringing together the power of several tools, including Gemini, FastAPI, and Pydantic. buff.ly/3U6BAFZ

'Below is a summary of what I’ve learnt and applied working as a data professional in a start-up (Digivizer), a scale-up (Immutable), and a big tech company (Facebook) across a range of different products.' by Robbie Geoghegan buff.ly/4bBosAu

In their latest article, Elahe Aghapour and Salar Rahili provide an in-depth exploration of ways to integrate foundational models, such as LLMs and VLMs, into a RL training loop. buff.ly/4cU54Qh

'Simulation is a powerful tool in the data science tool box. In this article, we’ll talk about how simulating systems can help us formulate better strategies and make better decisions.'

Simulated Data, Real Learnings: Simulating Systems by Jarom Hulet buff.ly/3U2Po4l

Master advanced information retrieval: Damián Gil's recent post explored cutting-edge techniques to optimize the selection of relevant documents with LangChain to create excellent RAGs. buff.ly/3Ukxmff

In this article, Varun Joshi and Gauri Kamat show how the Entity Resolution (ER) framework helps solve the Product Matching (PM) problem. 'Specifically, we describe a framework widely used in ER, and demonstrate its application on a synthetic PM dataset' buff.ly/3URHz20

In a recent article, Conor O'Sullivan explored 8 plots that can make linear regression more accessible, including residual plots, mean effect plot, and SHAP values for linear models. buff.ly/3xHlATx

'In this article, we’ll explore AlexNet, a groundbreaking CNN architecture that has significantly influenced the field of computer vision.'

The Math Behind Deep CNN — AlexNet by Cristian Leo buff.ly/49END39

'Over the years since then, I’ve grown convinced that an ownership mentality is one of the key things that sets high performers apart from their peers.'

Tessa Xie recently wrote about a mindset shift that can help you level up as a data scientist. buff.ly/3U0VHp1

Why not process your invoices with the help of machine learning? Jeremy Arancio's patient tutorial guides us through the steps of fine-tuning LayoutLM on your invoices, using the Transformers library, Label Studio, and AWS S3. buff.ly/3U8KHq2

What are the key components in managing a high-performing data science team? From effective prioritization to empathetic conflict management, zakraicik.xyz shares concrete insights based on his own experiences. buff.ly/4aXeUPC

In his new exploration of physics-informed neural networks (PINN), John Morrow explains how we can solve differential equations directly with neural networks, and provides a full code implementation. buff.ly/3WUXaAn

Learn how you can leverage scikit-learn's TunedThresholdClassifierCV class to optimize a decision threshold — Kevin Arvai walks us through the process step by step, and offers both potential use cases and the necessary code. buff.ly/3UYIFZN