January Is for Challenging Yourself to Learn New Skills
You can start your data science journey at any time; expanding your skill set should be an ongoing, yearlong process. Still, even those of us who are skeptical of new year’s resolutions can’t deny the sense of excitement and opportunity that comes with a whole, blank-slate year on the horizon. What better time to take the plunge and explore new topics?
To give you a helpful nudge in that direction, we’ve put together a lineup of fantastic articles from recent weeks that focus on accessible, practical approaches to machine learning and data workflows. Many of these are beginner-friendly, but as we often remind ourselves: you’re always a beginner when you decide to learn something new.
We hope you enjoy our selection this week, and that it inspires you to take on new challenges throughout the year. Let’s dive in.
- Courage to Learn ML: A Detailed Exploration of Gradient Descent and Popular Optimizers
In a new installement of her series of helpful machine learning explainers, Amy Ma offers a thorough and accessible guide to gradient descent and other optimizers, and focuses on choosing the right one depending on the task you’re aiming to complete. - From Adaline to Multilayer Neural Networks
If you feel like you’re not entirely on firm footing when it comes to all those complicated mathematical notations in machine learning papers, Pan Cretan’s latest deep dive is an excellent resource. It goes back to the early days of multilayer neural networks, builds one from scratch, and unpacks these networks’ mathematical descriptions. - A Comprehensive Overview of Gaussian Splatting
If you’re a more advanced practitioner who likes staying up-to-date with recent research, Kate Yurkova’s primer on Gaussian splatting is a must-read. It’s an ideal starting point for exploring this emerging approach for 3D representation and its various real-world use cases.
- LLM Agents — Intuitively and Exhaustively Explained
Ready to roll up your sleeves and start tinkering with large language models? Daniel Warfield recently shared an exhaustive, one-stop resource on LLM agents, how they work, and how they can help you streamline your interactions with AI tools. - A Bird’s Eye View of Linear Algebra: Systems of Equations, Linear Regression and Neural Networks
It’s never too late to bolster your math foundations or refresh your knowledge of concepts you only learned about in passing or a long time ago. The latest post in Rohit Pandey’s ongoing series on linear algebra takes a close look at solving systems of linear equations.
Still feeling energized? We hope so — here are a few other notable contributions we wanted to share with you this week:
- Diving deep into the problem of parameter efficiency, Mariano Kamp offers a detailed analysis of the design decisions that can help you get the most out of LoRA (low-rank adaptation).
- Whether you’re a data team lead or individual contributor, you’ll benefit from reading Ella Pham’s new article on the challenges of delivering tangible ROI—and how you can overcome them.
- Separating hype from real value is crucial when implementing generative-AI solutions at scale. Barr Moses shares five crucial insights that tech leaders should keep in mind.
- Is there a place for using AI tools as a job candidate? How should it affect hiring managers’ assessment of applicants? Christine Egan thoughtfully unpacks the stakes of a thorny topic.
- For a panoramic overview of LLMs’ progress in the past year and where the field is headed in 2024, don’t miss Zhaocheng Zhu’s excellent article (with coauthors Michael Galkin and others), which zooms in on the models’ reasoning capabilities.
- Bringing together some Python code, data visualization, and MIDI files (!), Dmitrii Eliuseev seeks to answer a less-simple-than-it-seems question: how many keys are actually needed to play the piano?
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