Towards Data Science

As organizations leverage storage accounts for data science and machine learning, implementing effective network access control becomes vital. Rene Bremer's article provides a comparison of service endpoints and private endpoints for security. #Azure #DataLake

One of the biggest benefits of agentic systems is their potential to overcome the cognitive limitations of individual models by breaking down tasks into specialized components. Read more from Tula Masterman's article! #GenAI #LLM

Contouring data on maps is common practice for many professions, including geology, meteorology, economics, and sociology. Learn how to make proximity maps with Python from Lee Vaughan now. #Python #DataScience

Niklas Lang explains all aspects related to ARIMA models, starting with a simple introduction to time series data and its special features. Read more now! #TimeSeries #Python

"In this article, we’ll delve into four critical reasons why you shouldn’t instinctively trust a statistically significant finding. Moreover, why you shouldn’t habitually discard non-statistically significant results." The Statistical Significance Scam by Cai Parry-Jones

"I’ve been a data scientist for over 3 years. This is what most people want to know about the field." Top Data Science Career Questions, Answered by Haden Pelletier

Core AI For Any Rummy Variant - Step by Step guide to a Rummy AI 🖋️ by Iheb Rachdi

BatchNorm is known to make a deep neural network converge faster and make the network stable at a higher learning rate. Can integrating BatchNorm in transformer-based architectures produce similar effects? In this recent article, I investigate this question in the specific context of a Vision Transformer. It seems that BatchNorm can have very similar effects in a Vision Transformer, provided the encoder is sufficiently deep. https://lnkd.in/eqpyvmBp

👋  Happy to share that my recent article has been accepted and published by Towards Data Science publication on Medium, along with a feature on their Editor's pick section. This publication has been my go-to source for AI-related learnings in recent years, and I am thrilled to have my article published here for the 2nd time. ✍ Please refer to the updated link to read my article, where I dive into my journey as an engineer transitioning into the world of LLM-native applications. If you like it or have any inputs to share, kindly leave a comment : https://lnkd.in/gcx9qBrs Stay tuned for part 2 where I will talk more aspects of my journey and share the best practices I picked up along the way. 🙂

Data science practitioners face unique challenges when dealing with diverse data types. Enter K-Fold Target Encoding, which replaces categorical labels with target-mean values, streamlining processing and enhancing model accuracy. Read more from Fhilipus Mahendra. #MachineLearning #DataEngineering