The process of reinforcement learning (RL) involves trial and error; rewarding actions; and remembering past experiences overtime. This technique is used when building sequential decision-making solutions like automated self-driving cars, video games or personalized content recommendations.  However, some of the challenges in building reinforcement learning models is it takes a long time for the system to learn and getting a high accuracy.  In this session, we'll explore different reinforcement learning solutions like how to implement relevant user experiences that improve over time, based on behavior using a pre-built API; and how to build your custom model from scratch in python while increasing the learning speed and final performance.

Instructor's Bio

Ruth Yakubu

Sr. Cloud Advocate at Microsoft

Ruth Yakubu is a Sr. Cloud Advocate at Microsoft; and also a tech startup founder. Ruth specializes in Java, Cloud, Advanced Analytics, Data Platforms and Artificial Intelligence (AI).

In addition she's been a tech speaker at several conferences like Microsoft Ignite, O'reilly velocity, Devoxx UK, Grace Hopper Dublin, TechSummit, Websummit, DeveloperWeek, JavaWithBest, DevNexus and several developer conferences. Prior to Microsoft, She has also worked for great companies like UNISYS, ACCENTURE and DIRECTV over the years where she gained a lot of experience with software architectural design and programming. She’s awarded’s Most Valued Blogger.

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    Making smart decisions in real-time with Reinforcement Learning

    • Webinar recording