When Tackling Complex Topics, the First Step Is the Hardest
Being a “beginner” isn’t a finite state you pass through once and leave behind forever. As long as you’re committed to continuous learning and growth, you’ll find yourself grappling with new concepts and ideas years (even decades!) into your career.
That’s a good thing—as long as you have solid guidance and the right resources to help you work your way through complex technical topics, like the ones data and machine learning professionals tackle in their workflows daily. That’s where we come in: TDS authors are at their best when they unpack cutting-edge research and tools and make them accessible for others, whether the reader is fresh out of their first bootcamp or a senior practitioner at a tech giant.
This week, we’ve selected several recent articles that do a precisely that: they cover a wide range of topics—from linear algebra to image classification—from the perspective of a supportive teacher who doesn’t assume too much prior knowledge on the part of their students. They offer concrete, actionable information, and keep it easy to absorb and digest without watering it down. Happy learning!
- What Is Generative AI? A Comprehensive Guide for Everyone
Almost a year since the launch of ChatGPT, generative AI has certainly gone mainstream—and so has confusion and misunderstanding about its inner workings, potential benefits, and current limitations. Mary Newhauser’s primer is a solid resource for anyone who needs a strong foundation in this timely topic. - Multi-Dimensional Exploration Is Possible!
When used well, analogies are a powerful tool for translating difficult concepts into digestible ideas. Diego Manfre’s recent explainer is a case in point: it unpacks the math behind principle component analysis (PCA) by comparing it to moving across and between dimensions (the great illustrations help, too). - Image Classification For Beginners
There are many ways to learn about essential machine learning workflows; for her introduction to image classification, Mina Ghashami chose to go back in time to 2014–2015, when two groundbreaking architectures—ResNet and VGG Network—were introduced. If you absorb knowledge better by digging into the context behind the topic at hand, this one’s for you.
- Linear Algebra 3: Vector Equations
For the past several weeks, tenzin migmar (t9nz) has been sharing beginner-friendly tutorials around the basics of linear algebra. The latest installment, which focuses on vector equations, is a great resource for people who have previously found the topic daunting, or for seasoned data professionals who might benefit from a refresher. - Support Vector Machine with Scikit-Learn: A Friendly Introduction
If you’re still in the process of building your core machine learning skills, adding a powerful algorithm to your toolkit is a great idea. Riccardo Andreoni’s guide on support vector machines does an excellent job balancing the theoretical background behind SVM and the practical aspects of using them in your work. - Transformers — Intuitively and Exhaustively Explained
We’ve published many well-executed articles on the transformer architecture over the years, but there’s always room for another approach. Daniel Warfield’s detailed overview takes the model apart to reveal its building blocks, and spends time on how each works separately as well as in relation to each other.
We hope you have some extra time to read a few of our other recent standouts—they cover a lot of ground, and do it really, really well:
- Yennie Jun investigated GPT-4’s math skills, and zoomed in on the discrepancies between its performance in English and in traditionally under-resourced languages.
- AI tools are popping up at a dizzying pace, and for Sam Stone that means it’s time we devote much more attention to their UI/UX design.
- What’s new in Colab? The Google notebook service has been around for a while, and Parul Pandey’s overview helps data scientists stay up-to-date on its latest product enhancements.
- Handling “megemodels” comes with its own set of challenges and pain points; Amber Teng’s hands-on deep dive shows how to load the popular Llama 2 model across different configurations.
- Following up on his earlier work on innovative prompt-engineering techniques, Giuseppe Scalamogna provides a roadmap for crafting different types of program simulation prompts.
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