An Overview to Cloud AI
Google Cloud AI aims to democratize AI by helping businesses generate actionable intelligence from data to create customer value. No matter where you are in your ML journey, join us for an overview of Cloud AI to see how it can provide you with the capability to move from data to insight to action, using pre-trained machine learning models, tools to generate and host your own models, and out-of-the-box ML solutions.
Reinforcement Learning for Recommendation system
Recommender systems are crucial for online services to match users with personalized and contextualized content from a large collection of items. Most existing systems are based on supervised learning and are inherently myopic—estimating a user’s immediate response to a recommendation without considering the long-term impact on subsequent user behavior. Recently, reinforcement learning has shown promise in several fields like games and robotics. However, applying existing methods to large-scale production recommender systems with millions to billions of users and items involves many challenges.
In this talk, we'll discuss how to use reinforcement learning for large-scale sequential recommender systems—where the systems learn from trajectories of sequential interactions with users, and take a sequence of actions to optimize for long-term values, not just immediate rewards—all while satisfying real-world training and serving constraints. We'll present recent research results, and discuss future opportunities for improvement.
What's new in TensorFlow?
Machine Learning is one of the most in-demand technologies and one of the fastest growing frameworks for machine learning is TensorFlow. TensorFlow is Google’s open source machine learning platform that is most widely adopted by machine learning developers worldwide.
TensorFlow 2.0 was announced earlier this year. It is easy, more powerful, and scalable. There are easier APIs with better code examples and documentation. This session will show you the main differences and the major benefits of TF 2.0.
LSTM-Based Online Handwriting Recognition
Google launched Google Handwriting Input in 2015, which enabled users to handwrite text on their Android mobile devices. Since then, progress in machine learning has enabled new model architectures and training methodologies, allowing us to build a new machine learning model and reduces error rates substantially compared to the old version. We will give an overview on the latest released "Fast Multi-language LSTM-based Online Handwriting Recognition" and explain how it works.