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YOLO — Intuitively and Exhaustively Explained
YOLO — Intuitively and Exhaustively Explained
The genesis of the most widely used object detection models.
Daniel Warfield
May 31
Stepping out of the “comfort zone“ through domain adaptation — a deep-dive into dynamic prompting…
Stepping out of the “comfort zone“ through domain adaptation — a deep-dive into dynamic prompting…
Part 2/3 — Deep dive into in-context learning
Aris Tsakpinis
May 31
Latest
Deep Dive into Anthropic’s Sparse Autoencoders by Hand ✍️
Deep Dive into Anthropic’s Sparse Autoencoders by Hand ✍️
Explore the concepts behind the interpretability quest for LLMs
Srijanie Dey, PhD
May 30
Writing Powerful Programming Articles: A Guide for Success
Writing Powerful Programming Articles: A Guide for Success
Reflections on 4+ Years of Publishing Programming Articles
Amanda Iglesias Moreno
May 30
Long Short Term Memory (LSTM)— Improving RNNs
Long Short Term Memory (LSTM)— Improving RNNs
How state of the art RNNs work
Egor Howell
May 30
Orchestrating a Dynamic Time-series Pipeline with Azure Data Factory and Databricks
Orchestrating a Dynamic Time-series Pipeline with Azure Data Factory and Databricks
Explore how to build, trigger and parameterize a time-series data pipeline in Azure, accompanied by a step-by-step tutorial
John Leung
May 30
Train Naive Bayes … really fast
Train Naive Bayes … really fast
Performance tuning in Julia
Roland Schätzle
May 30
Improve LLM output reliability with Python guardrails
Improve LLM output reliability with Python guardrails
Leverage validation functions to prevent your LLM outputs from falling of a cliff
Jan Majewski
May 30
Terraforming Dataform
Terraforming Dataform
Dataform 101, Part 2: Provisioning with Least Privilege Access Control
Kabeer Akande
May 30
Computing Minimum Sample Size for A/B Tests in Statsmodels: How and Why
Computing Minimum Sample Size for A/B Tests in Statsmodels: How and Why
A deep-dive into how and why Statsmodels uses numerical optimization instead of closed-form formulas
Jason Jia
May 30
Data Science Skills 101: How to Solve Any Problem, Part II
Data Science Skills 101: How to Solve Any Problem, Part II
Six simple techniques you can apply in real life
Josh Taylor
May 30
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