Introduction
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Course overview
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Getting started with deep learning
Introduction to deep learning, situations in which it is useful, key terminology, industry trends, and challenges.
Unlocking New Capabilities
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Biological inspiration for deep neural networks (DNNs)
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Training DNNs with big data
Hands-on exercise: training neural networks to perform image classification by harnessing the three main ingredients of deep learning: deep neural networks, big data, and the GPU
Hands-on exercise: deployment of trained neural networks from their training environment into real applications.
Measuring and Improving Performance
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Optimizing DNN performance
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Incorporating object detection
Hands-on exercise: neural network performance optimization and applying DNNs to object detection.
Summary
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Summary of key learnings
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Review of concepts and practical takeaways
Assessment
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Assessment project: train and deploy a deep neural network
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Validate learnings by applying the deep learning application development workflow (load dataset, train and deploy model) to a new problem.
Next Steps
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Workshop survey
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Setting up your own GPU-enabled environment
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Additional project ideas
Learn how to setup your GPU-enabled environment to begin work on your own projects. Explore additional project ideas along with resources to get started with NVIDIA AMI on the cloud, nvidia-docker, and the NVIDIA DIGITS container.
Tools, libraries, and frameworks: Caffe, DIGITS