Title: | Real World Reinforcement Learning in Action |
Date: | 9:00am-12:00pm, Oct. 10th, Thursday |
Instructor: | Tyler Clintworth and Microsoft Team |
Outline: |
Microsoft recently announced the Azure Cognitive Service, Personalizer, aimed at democratizing real world reinforcement learning for content personalization. Its goal is to make reinforcement learning accessible to everyone, not just machine learning experts. Personalizer is the result of a successful partnership between Microsoft Research and Azure Cognitive Services aimed at rapid technology transfer and innovation.
In this workshop you will learn the theory behind reinforcement learning, contextual bandits and how this applies to content personalization. We will walk you through setting up the service, writing your first application, and optimizing the policy using offline optimization.
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Who should learn: | Developers, Data Scientists who are working on machine learning, deep learning. |
Difficulity Level: | Beginner to Intermediate |
Prerequsite: | * Python and basic knowledge on machine learning * Bring laptop (and charger) |
Title: | AI and Machine Learning at Any Scale |
Date: | 1:00pm-4:00pm, Oct. 10th, Thursday |
Instructor: | Robert Nishihara, UC Berkeley |
Outline: |
Surprisingly, there is no simple way to scale up machine learning seamlessly from your laptop to the cloud. Someone developing an ML or data processing application on his or her laptop may parallelize the application across 4 or 8 cores but will then invariably hit a wall. Scaling up to the cloud often requires rewriting and rearchitecting the application using complex low-level tools like Kubernetes and GRPC or high-level but domain specific tools like Spark and Horovod. Ray is an open source framework for parallel and distributed computing and reinforcement learning. We lead a deep dive into Ray, walking you through its API and system architecture and sharing application example. |
Who should learn: | Developers, Data Scientists who are working on machine learning, deep learning. |
Level: | Beginner to Intermediate |
Prerequsite: | * Python and basic knowledge on machine learning * Bring laptop (and charger) * This will involve programming in a Jupyter notebook through a browser. No need to install anything |
Title: | Serverless Machine Learning with TensorFlow 2.0 |
Date: | 9:00am-4:00pm, Oct. 11th, Friday |
Instructor: | Lak, Tech Lead from Google. Lak, is a Tech Lead for Data and Machine Learning at Google Cloud where he enables customers of Google Cloud to be successful with Big Data and Machine Learning products through courses, consulting, evangelism, and open-source examples. He is the author of Data Science on GCP (O'Reilly) and Machine Learning on GCP (Coursera). He founded Google's Advanced Solutions Lab ML Immersion program. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA. Follow him on Twitter at @lak_gcp, read articles by him on Medium, and see more details at www.vlakshman.com |
Course Outline: |
This workshop provides a hands-on introduction to designing and building machine learning models on structured data on Google Cloud Platform. You will learn machine learning (ML) concepts and how to implement them using both BigQuery Machine Learning and TensorFlow/Keras. You will apply the lessons to a large out-of-memory dataset and develop hands-on skills in developing, evaluating, and productionizing ML models. Outline: (each of module is about 45 minutes and consists of 15 min lecture and 30 min codelab)
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Who should learn: | Developers, Data Scientists who are working on machine learning, deep learning. |
Level: | Beginner to Intermediate |
Prerequsite: | The following is prefered but not required.
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