Recommender Systems: An Applied Approach using Deep Learning
Let's build a customized recommender system or recommender engine using deep learning
Created by AI Sciences
Discover how to build effective recommender systems using deep learning and Python. You'll work with real-world datasets and learn practical techniques for developing, evaluating, and improving recommendation engines using a two-tower model approach. Gain hands-on experience with TensorFlow and modern collaborative filtering methods.
Packt | Feb 2023 | 121 min
What You Will Learn
You will start by exploring key deep learning concepts and how they apply to recommender systems. Through hands-on coding exercises and practical projects, you'll implement a two-tower model and use TensorFlow to build, test, and refine your own recommendation engine. Each step is guided with clear explanations and real data.
Key Features
- Develop and evaluate deep learning models for personalized recommendations
- Build and test a two-tower recommender system using TensorFlow Recommenders
- Analyze and prepare real-world data for effective recommendation results
Target Audience
This course is ideal for data scientists, machine learning practitioners, and developers with intermediate Python skills who want to create custom recommender systems. If you have experience with Python and Pandas and are eager to apply deep learning to real-world recommendation problems, you'll find practical value here.
