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The technical tagteam behind this blog. We aim to showcase the latest research, tools, and hardware for developing AI applications.

Scraping Smarter with Content Filtering

Scrapy is a powerful web scraping framework and essential tool for building machine learning datasets. When a site has a particularly simple structure, scrapy makes it easy to get a spider running to build up a curated dataset. Check out the tutorials in scrapy鈥檚 documentation. For example, to train a poster similarity model, we first needed to gather many movie posters. Consider trying to scrape IMDb.com.We may be interested in gathering posters from <img> tags under <div> tags with the class "poster"....

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Content-Based Video Recommendations with Metric Learning

This year marks a sharp increase in the use of streaming services. Many platforms use video posters as the main representation of content to watch. Naturally, the visual representation strongly influences a user鈥檚 propensity to watch the title. In fact, posters are designed to signal theme, genre and era. There are many theories on how poster elements can convey an emotion or capture attention. Netflix conducted a UX study, using eye tracking to find that 91% of titles are rejected after roughly 1 second of view time....

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TF-Recommenders & Kubernetes for flexible RecSys Model Development & Deployment

Introducing TF-Recommenders Recently, Google open sourced a Keras API for building recommender systems called TF-Recommenders. TF-Recommenders is flexible, making it easy to integrate heterogeneous signals like implicit ratings from user interactions, content embeddings, or real-time context info. This module also introduces losses specialized for ranking and retrieval which can be combined to benefit from multi-task learning. The developers emphasize the ease-of-use in research, as well as the robustness for deployment in web-scale applications....

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TF-Ranking and BERT for Movie Recommendations

Check out our repo for all the code referenced in this blog! Recommender systems are used by many groups to maximize the presentation of products to users. There is a variety of implementations for building recommender systems, but at their core, these systems are designed to sort a universe of items by their relevance to a user based on user information, item information, or both. One well known algorithm for solving the sorting problem is the Learn-to-Rank model, where the objective is to rank a list of examples by each item鈥檚 relevance to a particular user....

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IVA Pipelines with NVIDIA TLT and Deepstream SDK 5.0

We have seen applications in industries like retail, telemedicine, and robotics enabled by video analytics with machine learning. ML practitioners often leverage transfer learning with pretrained models to expedite development. Computer vision applications can benefit from using video analytics frameworks to facilitate faster iteration and experimentation. NVIDIA鈥檚 TLT toolkit and the Deepstream SDK 5.0 have made it easy to experiment with various network architectures and quickly deploy them on a NVIDIA powered device for optimized inference....

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