Five-Project Series

Real-World Deep Learning Recommender System you own this product

basics of linear algebra • 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
implement and train a recommendation system • split a single recommendation system into two essential parts (retrieval and ranking) • understand what Feature Stores are and how to use them • use exploration methods to improve recommendations
Shaked Zychlinski
5 weeks · 5-7 hours per week average · ADVANCED

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5, 10 or 20 seats+ for your team - learn more

Bridge the gap between the recommender system theory you’ve learned and the hands-on experience you need. This liveProject series provides a deep dive into real-world data management in industrial applications that’s truly rare in learning resources. Developing real-world recommendation systems is much more than understanding how to connect neurons in a neural network and knowing different types of architectures. The true complexity of these systems lies in understanding how to design them to fit real-time use on industry servers, taking into account ranking of items, splitting to offline and online parts, and, most importantly, performing exploration. The advanced, comprehensive liveProjects in this series will provide data scientists with insights—usually learned on the job!—that will take their careers to the next level.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

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  • Real-World Deep Learning Recommender System project for free

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.


This liveProject series 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:

  • Intermediate Python (NumPy, pandas, Matplotlib)
  • Intermediate scikit-learn
  • Intermediate TensorFlow 2.x (Keras interface)
  • Basic linear algebra (vectors, spaces, matrix transformations)
  • Intermediate data science
  • Intermediate machine learning, including deep learning

you will learn

In this liveProject series, you’ll learn to develop a real-world recommendation system:

  • Parse and engineer features using Python built-in libraries and external ones
  • Use NLP to prepare the data and design features’ vocabularies for future embeddings
  • Discover useful baselines, compute them and set the minimum requirements to any future model
  • Implement and train an entire recommendation system using the TensorFlow Recommenders framework
  • Reconfigure a “notebook-only” recommendation system to a model capable of quickly and efficiently handling many items per user
  • Use linear algebra to combine networks with differing dimensionality
  • Split a single recommendation system into two essential parts, retrieval and ranking, using TensorFlow Recommenders
  • Understand what Feature Stores are and how to use them
  • Use sampling techniques as exploration methods after model predictions have been made
  • Using a Feature Store as part of a prediction pipeline


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