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towardsdatascience

@towardsdatascience@me.dm

A Medium publication sharing concepts, ideas, and codes. Share your insights and projects with our global audience: http://bit.ly/write-for-tds.

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In this article, Theo Wolf shares a new type of network that is making waves in the ML world: https://buff.ly/4ak8HMT

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Looking to roll up your sleeves and experiment with some code this weekend? Joe Sasson presents a detailed, step-by-step guide that will show you how to develop and deploy an entirely local RAG system — from scratch. https://buff.ly/3WwRahb

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"I will show you how we can easily implement a N-BEATS model in Python and also tune its hyperparameters."

Jonte Dancker shares an easy-to-understand deep dive into how N-BEATS works and how you can use it: https://buff.ly/3QHIrF1

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In a clear and accessible deep dive, Harrison Hoffman explores the intricacies of early stopping from the perspective of training-data quality. https://buff.ly/3QGPayW

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What are the skills that help data scientists stand out? Don't miss our weekly highlights, which tackle this question from several angles, with contributions by Tessa Xie, Zachary Raicik, Hennie de Harder, and Eryk Lewinson. https://buff.ly/4dyTOsQ

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In a thoughtful career-focused deep dive, Sara Nóbrega shares her personal journey and offers practical tips and advice for making a successful transition from physics to data science. https://buff.ly/4ahemD8

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In the latest installment of his "Math behind..." series, Cristian Leo leads us on a detailed exploration of batch normalization, its underlying mathematics, and its from-scratch implementation. https://buff.ly/3JQfu62

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How should we approach the moral judgments produced by chatbots like ChatGPT?

Based on their recent research, Eyal Aharoni and Eddy Nahmias unpack the ethical and social questions at stake. https://buff.ly/44xAYOI

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LLMs Pitfalls - An introduction to some of the key components surrounding LLMs to produce production-grade applications by Pier Paolo Ippolito https://towardsdatascience.com/llms-pitfalls-7a33de009638

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Writing at the intersection of bioinformatics, molecular biology, and AI, Murto Hilali explains how to use AlphaFold-Multimer, XGBoost, and 47,000 SLURM jobs to predict PPI outcomes with 91% AUC. https://buff.ly/3WtYKZS

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To demonstrate the power of a fine-tuned Llama 3 model, Robert A. Gonsalves walks us through his latest project, in which he created a multilingual fanfic writing assistant. https://buff.ly/3UOvzPQ

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In a thoughtful and timely article, Rachel Draelos frames and explores the hypothesis that general intelligence arises when a learning system becomes very good at next token prediction. https://buff.ly/4dgzuMJ

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In a concise guide, Christopher Tao walks us through the benefits of using Python caching techniques to boost the performance of your code. https://buff.ly/44jGSms

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For an accessible and thorough introduction to methods for calculating customer lifetime value, don't miss the final installment in Katherine Munro's excellent series on the different facets of CLV. https://buff.ly/44nAESx

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What are the environmental costs we pay for developing and using innovative AI tools? Stephanie Kirmer tackles one of the most timely and complex questions ML and data professionals face these days. https://buff.ly/3WpLzZX

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From RAG pipelines to a new coefficient of correlation, we're thrilled to share some of our most popular articles from the past month, including contributions by Shaw Talebi, Cristian Leo, Anna Zawadzka, Tim Sumner, Leon Eversberg, Srijanie Dey, Anna Via, Youness Mansar, and Mariya Mansurova. https://buff.ly/4b1YYvO

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Take a look under the hood of the new Llama 3 model by following along Srijanie Dey, Edurado Ordax, and Tom Yeh's lucid explainer on its transformer architecture. https://buff.ly/3UL3Boc

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In a comprehensive guide, Amy Ma continues to explore the challenges of vanishing and exploding gradients and shows how activation functions, weights initialization, and batch normalization can help us address them. https://buff.ly/4bFRsqv

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Get Underlined Text from Any PDF with Python - In this article, Sasha Korovkina shares a step-by-step guide to get underlined text as an array from PDF files: https://buff.ly/3Qx4r5E

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Data science career paths are often full of unexpected twists and turns. Matt Chapman shares his, and reflects on the benefits of a humanities or social sciences background for becoming a successful data professional. https://buff.ly/44xEYyM

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"LLMs are already being used on e-commerce platforms to improve the search and recommendation process. But what happens if this very LLM powering the recommendations is manipulated?"

Parul Pandey explores the intersection of LLMs and e-commerce practices. https://buff.ly/3QofAWq

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In a comprehensive, hands-on guide, Agustinus Nalwan leverages the power of LLMs to enhance the quality of document context retrieved for direct-answer generation in your RAG setup. https://buff.ly/4aUKeik

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Discover the BiTCN model for multivariate time series forecasting, explore its architecture, and implement it in Python - 🖋️ by Marco Peixeiro https://buff.ly/4aZjjBP

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In part two of her excellent series on computer simulations for product analysts, Mariya Mansurova provides a thorough, hands-on guide to using bootstrap for observations and A/B tests. https://buff.ly/3QmGTjG

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By taking a close look at the evolution of human intelligence, could we perhaps gain a better understanding of AI's potential — and limitations?

Stephanie Shen's latest deep dive explores one of the most fascinating questions at the intersection of neuroscience and technology. https://buff.ly/3JEDtVV

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