Vince Lam

Vince Lam

Data Science & AI Engineering

Day 1: Why I’m Building a Daily Online Writing Habit

1 minute read

Published:

I recently signed up for Dickie Bush & Nicolas Cole’s cohort-based course, Ship 30 for 30.

Here are a few reasons why:

  1. Writing is a useful and timeless skill in all areas of life: It improves communication, structures thoughts, and serves as an outlet for self-expression, whether it’s personal journaling, professional documentation, or persuasive storytelling. I’m excited to learn the frameworks from the best online writers for different platforms.

  2. Quantity Breeds Quality: By writing daily and sharing online, I’ll create more data points to learn from real-world feedback. This data-driven approach uses quantity as a forcing function for quality, learning what works and what doesn’t.

  3. Expand My Luck Surface Area: Writing online has already opened doors to freelance data science work for me. I can increase my chances of serendipitous encounters with a larger library of work. Create a 24/7 opportunity flywheel.

  4. Overcome Fears through Accountability: I have countless ideas and insights to share, but I’ve struggled with the fear of judgement and perfectionism. Joining a supportive global community of like-minded writers will keep me accountable and inspired. Imperfect and published is better than “perfect” but unpublished—just ship.

Over the next 30 days, I plan on writing about Generative AI, freelance data science, and productivity.

What Are Embeddings and Why They’re Incredibly Useful

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If you’ve heard of the term “embeddings” before but don’t quite understand it, this post is for you. I’ll provide an intuitive explanation of embeddings and their significance.

Traditional Natural Language Processing (NLP) Tasks: Understanding What Large Language Models (LLMs) Can Do For You

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Since Transformers were introduced in 2017, Natural Language Processing (NLP) capabilities have skyrocketed. Knowing traditional NLP tasks is useful to understand the capabilities of (Large) Language Models. Although LLMs may outperform in many areas, smaller fine-tuned models will always have a place due to their faster latency and lower costs.