Deep Learning for Recommender Systems (Nick Pentreath)
Nick Pentreath, a Principal Engineer at IBM, explains how in the last few years, deep learning has achieved significant success in a wide range of domains, including computer vision, artificial intelligence, speech, NLP, and reinforcement learning. However, deep learning in recommender systems has, until recently, received relatively little attention. This talks explores recent advances in this area in both research and practice. I will explain how deep learning can be applied to recommendation settings, architectures for handling contextual data, side information, and time-based models, and compare deep learning approaches to other cutting-edge contextual recommendation models, and finally explore scalability issues and model serving challenges.
Learn more here: https://databricks.com/session/deep-learning-for-recommender-systems
Article you might like: https://databricks.com/session/cloud-cost-management-and-apache-spark
About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.
Read more here: https://databricks.com/product/unified-data-analytics-platform
Connect with us:
Website: https://databricks.com
Facebook: https://www.facebook.com/databricksinc
Twitter: https://twitter.com/databricks
LinkedIn: https://www.linkedin.com/company/databricks
Instagram: https://www.instagram.com/databricksinc/ Databricks is proud to announce that Gartner has named us a Leader in both the 2021 Magic Quadrant for Cloud Database Management Systems and the 2021 Magic Quadrant for Data Science and Machine Learning Platforms. Download the reports here. https://databricks.com/databricks-named-leader-by-gartner
Nick Pentreath, a Principal Engineer at IBM, explains how in the last few years, deep learning has achieved significant success in a wide range of domains, including computer vision, artificial intelligence, speech, NLP, and reinforcement learning. However, deep learning in recommender systems has, until recently, received relatively little attention. This talks explores recent advances in this area in both research and practice. I will explain how deep learning can be applied to recommendation settings, architectures for handling contextual data, side information, and time-based models, and compare deep learning approaches to other cutting-edge contextual recommendation models, and finally explore scalability issues and model serving challenges.
Learn more here: https://databricks.com/session/deep-learning-for-recommender-systems
Article you might like: https://databricks.com/session/cloud-cost-management-and-apache-spark
About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.
Read more here: https://databricks.com/product/unified-data-analytics-platform
Connect with us:
Website: https://databricks.com
Facebook: https://www.facebook.com/databricksinc
Twitter: https://twitter.com/databricks
LinkedIn: https://www.linkedin.com/company/databricks
Instagram: https://www.instagram.com/databricksinc/ Databricks is proud to announce that Gartner has named us a Leader in both the 2021 Magic Quadrant for Cloud Database Management Systems and the 2021 Magic Quadrant for Data Science and Machine Learning Platforms. Download the reports here. https://databricks.com/databricks-named-leader-by-gartner
