Yanir Seroussi – AI/ML Success Architect

Insights on data collection and machine learning from spending a month sailing, diving, and counting fish with Reef Life Survey.

Some companies present raw data or information as “insights”.

Defining feasible problems and coming up with reasonable ways of measuring solutions is harder than building accurate models or obtaining clean data.

Migrating BCRecommender from MongoDB to Elasticsearch made it possible to offer a richer search experience to users at a similar cost, among other benefits.

Nutritionism is a special case of misinterpretation and miscommunication of scientific results – something many data scientists encounter in their work.

Giving an overview of the field and common paradigms, and debunking five common myths about recommender systems.

Hiring data scientists prematurely is wasteful and frustrating.

Migrating my web apps away from Parse.com due to reliability issues.

Progress on my album cover classification project, highlighting lessons that would be useful to others who are getting started with deep learning.

Useful posts and papers on the topic of deep learning (circa 2015).

To become proficient at solving data science problems, you need to get your hands dirty.

I became a data scientist by doing a PhD, but the same steps can be followed without a formal education program.

Recent choices I’ve made to reduce my exposure to fossil fuels, including practical steps that can be taken by Australians and generally applicable lessons.

An overview of my PhD in data science / artificial intelligence.

Progress since leaving my last full-time job and setting on an independent path that includes data science consulting and work on my own projects.

My team’s solution to the Yandex Search Personalisation competition (finished 9th out of 194 teams).

Insights on search personalisation and SEO from participating in a Kaggle competition (finished 9th out of 194 teams).

A script for importing data into the Parse backend-as-a-service.

Exploring an approach to choosing the optimal number of iterations in stochastic gradient boosting, following a bug I found in scikit-learn.

Increasing SEO traffic to BCRecommender by adding content and opening up more pages for crawling.

Summary of a Kaggle competition to forecast bulldozer sale price, where I finished 9th out of 476 teams.

Update on BCRecommender traction using three channels: blogger outreach, search engine optimisation, and content marketing.

Data science has been a hot term in the past few years.

Summary of my approach to the Greek Media Monitoring Kaggle competition, where I finished 6th out of 120 teams.

Ranking 19 channels with the goal of getting traction for BCRecommender.

The recommendation backend for my BCRecommender service for personalised Bandcamp music discovery.

Iterating on my BCRecommender service with the goal of keeping costs low while providing a valuable music recommendation service.

My motivation behind building BCRecommender, a free recommendation & discovery service for Bandcamp music.

Summary of a talk I gave at the Data Science Sydney meetup with ten tips on almost-winning Kaggle competitions.

Discussing the hierarchy of needs proposed by Jay Kreps.

Pointers to all my Kaggle advice posts and competition summaries.

First post!