Recommender systems are used in various areas with commonly recognized examples, including playlist generators for video and music services, product recommenders for online stores and social media platforms, and open web content recommenders. Recommender systems have also been developed to explore research articles and experts, collaborators, and financial services.
The course begins with an introduction to deep learning concepts to develop recommender systems and a course overview. The course advances to topics covered, including deep learning for recommender systems, understanding the pros and cons of deep learning, recommendation inference, and deep learning-based recommendation approach. You will then explore neural collaborative filtering and learn how to build a project based on the Amazon Product Recommendation System. You will learn to install the required packages, analyze data for products recommendation, prepare data, and model development using a two-tower approach.
You will learn to implement a TensorFlow recommender and test a recommender model. You will make predictions using the built recommender system.
Upon completion, you can relate the concepts and theories for recommender systems in various domains and implement deep learning models for building real-world recommendation systems.
All resources are available at: https://github.com/PacktPublishing/Recommender-Systems-An-Applied-Appro…
publication date
2023-02-22
what you will learn
Learn about deep learning and recommender systems
Explore the mechanisms of deep learning-based approaches
Learn to implement a two-tower model for recommenders
Implement TensorFlow to develop a recommender system
Learn basic neural network models for recommendations
Explore neural collaborative filtering and variational autoencoders
key features
Understand, implement, and evaluate deep learning models for building real-world recommendation systems * Validate, test, and make predictions using recommender systems with the help of TensorFlow Recommenders * Explore the benefits and challenges of deep learning in recommender systems
approach
The course is well-structured and explanatory. Every module has engaging content covering necessary theoretical concepts with practical explanations. The lectures are divided into many videos and comprehensively detailed code notebooks, providing a unique hands-on experience using a real-time project. This course is easily understandable, expressive, and self-explanatory, with live coding.
audience
This course is designed for individuals looking to advance their skills in applied deep learning, understand relationships of data analysis with deep learning, build customized recommender systems for their applications, and implement deep learning algorithms for recommender systems. Individuals passionate about recommender systems with the help of TensorFlow Recommenders will benefit from this course. Deep learning practitioners, research scholars, and data scientists will also benefit from the course. The prerequisites include a basic to intermediate knowledge of Python and Pandas library.
short description
This comprehensive course will help you learn how to use the power of Python to evaluate your deep learning-based recommender system data sets based on user ratings and choices with a practical approach to building a deep learning-based recommender system by adopting a retrieval-based approach based on a two-tower model.
subtitle
Let's build a customized recommender system or recommender engine using deep learning
keywords
recommender systems, two-tower model, TensorFlow, neural network models, neural collaborative filtering, variational autoencoders, Amazon Product Recommendation System
Product ISBN
9781837638062
