| Summary: | This course is a complete package for beginners to learn the basics of recommender systems and their applications and build them from scratch using deep learning with Python and the necessary concepts for the recommender system model. About This Video: 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. In Detail: 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 GitHub
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