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.
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:
In this liveProject series, you’ll learn to develop a real-world recommendation system:
geekle is based on a wordle clone.