Deep Learning Recommender System

Two Towers with TensorFlow Recommenders you own this product

This project is part of the liveProject series Real-World Deep Learning Recommender System
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.

book and video resources

When you start your liveProject, you get full access to the following books and videos for 90 days.

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

you will learn

In this liveProject, you’ll learn to develop a fully trained recommendation system:


  • 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

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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  • Two Towers with TensorFlow Recommenders project for free

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  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
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  • choose twelve free products per year
  • exclusive 50% discount on all purchases
  • Two Towers with TensorFlow Recommenders project for free