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REGISTER HERE >> http://bit.ly/meetup-ml-sf << Details: TensorFlow is an open source software library for high performance numerical computation that's great for writing models that can train and run on platforms ranging from your laptop to a fleet of servers in the Cloud to an edge device. This hands-on meetup takes you beyond the basics of using predefined models and teaches you how to build, train and deploy your own on GCP. Who should attend: Data Scientists, Machine Learning Engineers, Software Engineers, DevOps Engineers etc. who have basic programming skills and are interested in Machine Learning and Google Cloud. Agenda: HANDS-ON LABS 1. Machine Learning with TensorFlow In this lab you will learn how to use Google Cloud Machine Learning and Tensorflow to develop and evaluate prediction models using machine learning. Duration: 1 hour 15 minutes 2. TensorFlow for Poets In this lab you will learn how to install and run TensorFlow on a single machine, then train a simple classifier to classify images of flowers. Duration: 1 hour 3. Image Classification of Coastline Images Using TensorFlow on Cloud ML Engine In this lab, you carry out a transfer learning example based on Inception-v3 image recognition neural network. The objective is to classify coastline images captured using drones based on their potential for flood damage. Duration: 1 hour 30 minutes 4. Predict Housing Prices with Tensorflow and Cloud ML Engine In this lab you will build an end to end machine learning solution using Tensorflow + Cloud ML Engine and leverage the cloud for distributed training and online prediction. Duration: 1 hour 5. Creating an Object Detection Application Using TensorFlow This lab will show you how to install and run an object detection application. The application uses TensorFlow and other public API libraries to detect multiple objects in an uploaded image. Duration: 45 minutes Speaker: Vladimir Starostenkov, Machine Learning Architect Bio: Vladimir has 10+ years of experience in software development. Over the course of his career, he has been part of 15 successful project implementations. Vladimir specializes in artificial intelligence and machine learning, distributed systems design, NoSQL and Hadoop-based systems benchmarking, permissioned blockchains, data engineering, and development of data-centric apps. As an expert in NoSQL databases, he has authored a number of research papers, comparing the performance of Apache Cassandra, Redis, MongoDB, and Couchbase. Vladimir also serves as a trainer and a data evangelist. He is responsible for analyzing requirements, preparing training materials, and conducting training sessions. Vladimir is an active member of the Open Data Science Community.