Fundamentals of Deep Learning for Computer Vision

This workshop teaches you to apply deep learning techniques to a wide range of computer vision tasks through a series of hands-on exercises. You will get to work with widely-used deep learning tools, frameworks, and workflows by performing neural network training and deployment on a fully-configured GPU accelerated workstation in the cloud. After a quick introduction to deep learning to start the course, you will advance to building and deploying deep learning applications for image classification and object detection, followed by modifying the applications to improve their accuracy and performance, and finish by implementing the workflow that you have learned on a final project. At the end of the workshop, you will have access to additional resources to create new deep learning applications on your own.

Duration: 8 hours
Certification: Upon successful completion of this workshop, you will receive NVIDIA DLI Certification to prove subject matter competency and support professional career growth
Prerequisites: Familiarity with basic programming fundamentals, like functions and variables
Languages: English
Tools, libraries, and frameworks: Caffe, DIGITS

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Throughout 2019 XENON will be offering both Private and Public Workshops.
Interested? Contact us now!

LEARNING OBJECTIVES

At the conclusion of the workshop, you will have an understanding of the fundamentals of deep learning and be able to:

Implement common deep learning workflows, such as image classification and object detection
Experiment with data, training parameters, network structure, and other strategies to increase performance and capability of neural networks
Integrate and deploy neural networks in your own applications to start solving sophisticated real-world problems

WHY DEEP LEARNING INSTITUTE HANDS-ON TRAINING?

Learn how to build deep learning and accelerated computing applications across a wide range of industry segments such as Autonomous Vehicles, Digital Content Creation, Finance, Game Development, and Healthcare
Obtain guided hands-on experience using the most widely used, industry-standard software, tools, and frameworks
Attain real world expertise through content designed in collaboration with industry leaders such as the Children’s Hospital of Los Angeles, Mayo Clinic, and PwC
Earn NVIDIA DLI Certification to prove your subject matter competency and support professional career growth
Access courses anywhere, anytime with a fully configured GPU-accelerated workstation in the cloud

Interested? Contact us now to book into the next course.

CONTENT OUTLINE

	Components	Description
Introduction (45 mins)	Course Overview Getting Started with Deep Learning	Introduction to deep learning, situations in which it is useful, key terminology, industry trends, and challenges.
Break (15 mins)
Unlocking New Capabilities (120 mins)	Biological inspiration for Deep Neural Networks (DNNs) 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.
Break (45 mins)
Unlocking New Capabilities (40 mins)	Deploying DNN models	Hands-on exercise: Deployment of trained neural networks from their training environment into real applications.
Break (15 mins)
Measuring and Improving Performance (100 mins)	Optimizing DNN Performance Incorporating Object Detection	Hands-on exercise: Neural network performance optimization and applying DNNs to object detection.
Break (15 mins)
Summary (20 mins)	Summary of Key Learnings	Review of concepts and practical takeaways.
Break (15 mins)
Assessment (60 mins)	Assessment Project: Train and Deploy a Deep Neural Network	Validate your learning by applying the deep learning application development workflow (load dataset, train and deploy model) to a new problem.
Next Steps (15 mins)	Workshop Survey Setting up your own GPU enabled environment Additional project ideas Getting Data	Learn how to setup your GPU-enabled environment to begin work on your own projects. Get additional project ideas along with resources to get started with NVIDIA AMI on the cloud, nvidia-docker, and the NVIDIA DIGITS container.