Eugene Yan

Asking LLMs to generate biographies to get a sense of how they memorize and regurgitate.

Writing good instructions to achieve high precision and throughput.

Collecting ground truth, data augmentation, cascading heuristics and models, and more.

End of week debrief, weekly business review, monthly learning sessions, and quarter review.

Pilot & copilot, literature review, methodology review, and timeboxing.

How to migrate and sync notes & images across devices

Seeking first to understand, earning trust, and preparing for away team work.

Travelled, wrote, and learned a lot, L5 -> L6, gave a keynote at RecSyS, and started a meetup.

A quick overview of variational and denoising autoencoders and comparing them to diffusers.

The fundamentals of text-to-image generation, relevant papers, and experimenting with DDPM.

My three favorite papers, 17 paper summaries, and ML and non-ML lessons.

Invited keynote at the Workshop for Online Recommender Systems and User Modeling (ORSUM)

Or why I should write fewer integration tests.

Pushing back on the cult of complexity.

Some off-the-beaten uses of Python learned from reading libraries.

15 minutes a week to document your work, increase visibility, and earn trust.

Understanding and spotting patterns to use code and components as intended.

Mindset, 100-day plan, and balancing learning and taking action to earn trust.

Industry examples, exploration strategies, warm-starting, off-policy evaluation, and more.

Introducing randomness and/or learning from inherent randomness to mitigate position bias.

Thinking about recsys as interventional vs. observational, and inverse propensity scoring.

How they differ and why they work better in different situations.

Hard-won lessons on how to start data science projects effectively.

I'm heading into a team lead role and would like to define the vision and roadmap.

What to consider for in terms of data, roadmap, role, manager, tooling, etc.

Beyond getting that starting role, how does one continue growing in the field?

Daliana and I had a 2hr chat on all things data science and machine learning.

Met most of my goals, adopted a puppy, and built ApplyingML.com.

More than two dozen interviews with ML Practitioners sharing their stories and advice

Susan shares 5 lessons she gained from writing online in public over the past year.

Write before you're ready, write for yourself, quantity over quality, and a few other lessons.

Simple baselines, ideas, tech stacks, and packages to try.

Why this is the first rule, some baseline heuristics, and when to move on to machine learning.

An overview of system design, candidate retrieval, and ranking, with industry examples.

Focusing on long-term rewards, exploration, and frequently updated item.

Why the Amazon applied scientist takes the time to break down his work for readers.

How to generate labels from scratch with semi, active, and weakly supervised learning.

Building semantic search; how to calculate recall when relevant documents are unknown.

Why real-time RecSys?

Show them the data, the Socratic method, earning trust, and more.

Breaking it into offline vs. online environments, and candidate retrieval vs. ranking steps.

A whirlwind tour of bandits, embedding+MLP, sequences, graph, and user embeddings.

My favourite project, how I write weekly and how you can start, and content I would like to see more of.

How to go from knowing machine learning to applying it at work to drive impact.

An overview and comparison of the various approaches, with examples from industry search systems.

Even high achieving individuals experience impostor syndrome; here's how Susan learned to manage it.

More education, achievements, and awards don't shoo away imposter syndrome.

What do you deeply care about?

We discussed about how to build and run data teams and engage better with business.

Mike and I take a philosophical detour on Talk Python and discuss life lessons from machine learning.