Deep Learning in Production Usin...

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Mar 28, 2017

Tue 9:00 AM

655 Montogomery Street, Ste. 400
San Francisco, CA 94111

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Course Objectives Leading corporations are betting their future on AI. In this 3-day hands-on workshop, you will learn best practices for neural network selection, tuning, training, and deployment into a production stack.  What You Will Achieve Choosing an appropriate network for data analysis problems is a complex decision. In this introductory course, we will guide you from problem evaluation, model selection to ETL and data pipelines. You will acquire practical industry knowledge and leave with the ability to build production grade, end-to-end deep learning products with Deeplearning4j. Who Should Attend? This course is designed for data engineers, ETL (extract, transform, and load) developers, and data scientists who need to interact with big data infrastructure. Course Prerequisites Attendees should be familiar with developing in Java (preferred) or Python. No prior knowledge of Deeplearning4j or neural networks is required. Agenda This three-day hands-on class will cover the topics listed below.  Day 1 History of Neural Networks How to Choose an Appropriate Neural Network Common Neural Network Architecture and Internals Day 2 Managing Data Pipelines and Data Injection with DataVec Building Neural Networks with Deeplearning4j Day 3 Tuning Neural Network Hyperparameters Training a Neural Networks Deploying Neural Networks in a Production Environment About Skymind Skymind is the company behind Deeplearning4j, the only commercial-grade, open-source, distributed deep-learning library written in Java and Scala. Used by Fortune 500 corporations, DL4J is specifically designed to run in business environments on distributed GPUs and CPUs. 

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