Getting a handle on the current state of machine learning is tricky: on the one hand, it takes time to catch up with foundational concepts and methods, even if you’ve worked in the field for a while. On the other hand, new tools and models keep popping up at a rapid clip. What’s an ML learner to do?
We tend to favor a balanced, cumulative approach—one that recognizes that no single person can master all the knowledge out there, but that digesting well-scoped pieces of information at a steady, ongoing cadence will help you gain a firm footing in the field.
Our selection of highlights this week reflects that belief: we’ve chosen a few well-executed articles that cover both essential topics and cutting-edge ones, and that both beginners and more seasoned professionals can benefit from reading. Let’s dive in.
SHAP vs. ALE for Feature Interactions: Understanding Conflicting Results
Making sense of model predictions is at the core of data professionals’ work, but it’s a process that is rarely straightforward. Valerie Carey’s latest article focuses on a particularly thorny scenario where two explainability tools—SHAP and ALE—produce conflicting results, and expands on how to move beyond these confusing moments.The Olympics of AI: Benchmarking Machine Learning Systems
Taking a cue from the athletes who first broke the 4-minute mile barrier, Matthew Stewart, PhD offers a panoramic overview of benchmarking in machine learning and explores how they facilitate innovation and improved performance: “A well-designed benchmark can guide a whole community toward breakthroughs that redefine a field.”Photo by Eranjan on UnsplashDINO — A Foundation Model for Computer Vision
If you learn best by diving deep into a topic, you don’t want to miss Sascha Kirch’s series, which unpacks and contextualizes influential machine learning papers, one model at a time. In a recent installation, Sascha walked us through the inner workings of DINO, a foundation model based on the groundbreaking abilities of visual transformers (ViT).Exploring GEMBA: A New LLM-Based Metric for Translation Quality Assessment
Machine translation isn’t exactly a novel technology, but the rise of LLMs has generated new possibilities for enhancing current tools and workflows. Dr. Varshita Sher’s latest article introduces us to GEMBA, a recently introduced metric that leverages the power of GPT models to evaluate the quality of machine-translated text.Machine Learning, Illustrated: Incremental Learning
For the visual learners out there, and especially those taking their first steps in the field, Shreya Rao’s beginner-friendly guide to incremental learning addresses a key question: how do models maintain and build upon existing knowledge?
In the mood for branching out into other topics this week? We hope so—here are a few other recent standouts:
If you find it difficult to make time in your busy schedule to explore new topics and expand your skill set, don’t miss Zijing Zhu’s guide to forming healthy continuous-learning habits as a data scientist.Bridging the gap between existing conversational-AI tools and real-world, user-facing UI systems is a real challenge; Janna Lipenkova’s deep dive provides a detailed roadmap to help you get there.What would a functional, useful AI ethics toolkit look like? Malak Sadek shares helpful insights based on a design-oriented workshop she recently led.For marketing- and business-focused data scientists, Damian Gil outlines several advanced customer-segmentation techniques (including one that relies on the power of LLMs) that can help you produce valuable insights.There are several attempts underway, by governments around the world, to regulate AI tools. Viggy Balagopalakrishnan reflects on their shortcomings, and advocates for a more pragmatic, mechanism-based approach.
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Machine Learning Must-Reads: Fall Edition was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
