Have you ever thought how YouTube adjust your feed as per your favorite content? Why is your Netflix recommending you your favorite TV shows? Have you ever wanted to build a customized deep learning-based recommender system for yourself? If Yes! Then this is the course you are looking for. You might have searched for many relevant courses, but this course is different!

From Amazon to Netflix to Pinterest, recommendation systems are the cornerstone of a majority of the modern-day billion-dollar industries. However, building recommender systems is not a straightforward task. What if we can build them in a few lines? Dropping the nitty-gritty details and concentrating on implementing algorithms with more ease is what any data scientist would like to get their hands on. Abstraction is a common trait amongst popular machine learning libraries or frameworks like TensorFlow.

From recommending movies or restaurants to coordinating fashion accessories and highlighting blog posts and news articles, recommender systems are an important application of machine learning, surfacing new discoveries and helping users find what they love. At Google, we have spent the last several years exploring new deep learning techniques to provide better recommendations through multi-task learning, reinforcement learning, better user representations and fairness objectives. These and other advancements have allowed us to greatly improve our recommendations. Today, we're excited to introduce TensorFlow Recommenders (TFRS), an open-source TensorFlow package that makes building, evaluating, and serving sophisticated recommender models easy. Built with TensorFlow 2.x, TFRS makes it possible to: TFRS is based on TensorFlow 2.x and Keras, making it instantly familiar and user-friendly.