Two Towers with TensorFlow Recommenders you own this product

prerequisites
intermediate Python data science libraries • intermediate machine learning, intermediate recommender system experience (specifically Two Towers) • basics of developing an ML pipeline, Intermediate TensorFlow 2.x
skills learned
discover useful baselines • compute baselines and set minimum requirements to future models • implement and train a recommendation system using the TensorFlow Recommenders framework
Shaked Zychlinski
1 week · 6-8 hours per week · ADVANCED
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Look inside

Once you’ve handled the data, the real magic can begin! In this liveProject, you’ll implement a basic recommendation system using the TensorFlow Recommenders framework—designed specifically for this purpose. First, you’ll calculate four baselines that your future models will have to beat. Next, you’ll learn the basics of developing a model using TensorFlow Recommenders, then design a simple, two-feature Two Towers model. Lastly, you’ll enhance this simple model by adding all the features you created in the previous liveProject while maintaining model performance that beats your four established baselines.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Shaked Zychlinski

Shaked is currently leading the recommendation research group and company’s recommendations efforts at Lightricks, developing the company's RS algorithms from the ground up. Prior to this, he worked at and led projects at the Algo group of Taboola, one of the largest content recommendation companies in the world. He is a featured writer on Towards Data Science, with hundreds of reads each day. He has also developed the Dython library for Python, with 26k (and counting) downloads a month.

prerequisites

This liveProject is for data scientists with theoretical knowledge of machine learning, deep learning, and recommender systems who want to take the next step in their career. To begin these liveProjects you will need to be familiar with the following:


TOOLS
  • Intermediate Python (NumPy, pandas, Matplotlib)
  • Intermediate scikit-learn
  • Basics of TensorFlow 2.x (Keras interface)
  • TensorFlow Recommenders (retrieval and ranking models)
TECHNIQUES
  • Basic linear algebra (vectors, spaces, matrix transformations)
  • Define, train, and evaluate models
  • Rank models using TensorFlow Recommenders

features

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