#100DaysOfML Reading Tracker

As part of my #100DaysOfMachineLearning Challenge, I have been reading a few technical blogs and academic papers. I have gotten a lot of requests to share the links to the stuff I am reading so I have decided to make a running post about the same.

This blog post will be updated periodically with links and references to the posts I mention on my Instagram.

Day 33 : Applying Deep Learning To Airbnb Search (Paper)

Day 32 : Embedding-based Retrieval in Facebook Search (Paper)

Day 31 : Assistive AI Makes Replying Easier (Blog)

Day 30 : Deep Learning Based Text Classification: A Comprehensive Review (Paper)

Day 28 : Building Riviera: A Declarative Real-Time Feature Engineering Framework (Blog)

Day 27 : Contextual and Sequential User Embeddings for Large-Scale Music Recommendation (Paper)

Day 26 : Retraining Machine Learning Models in the Wake of COVID-19 (Blog)

Day 25 : Embeddings@Twitter (Blog)

Day 24 : Unit Test Case Generation with Transformers and Focal Context (Paper)

Day 23 : Rules of Machine Learning: Best Practices for ML Engineering (Blog)

Day 21 : Using Deep Learning at Scale in Twitter’s Timelines (Blog)

Day 17 : Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition, Hang Li (Report)

Day 6 : The AI Behind LinkedIn Recruiter search and recommendation systems (Blog)