NVIDIA- Deep Learning Fundamentals with Computer Vision (DLF-CV)

Price
$500.00
Duration
 1 Day
Delivery Methods
 VILT    Private Group

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.

 

Upcoming Class Dates and Times

This class is not currently scheduled.
Contact us and we will help you get the training you need!

Course Objectives

  • 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

Agenda

1 - Components Description Introduction
  • Course Overview
  • Getting Started with Deep Learning
2 - Unlocking New Capabilities
  • Biological inspiration for Deep Neural Networks (DNNs)
  • Training DNNs with Big Data
3 - Unlocking New Capabilities
  • Deploying DNN models Deployment of trained neural networks from their training environment into real applications.
  • Measuring and Improving Performance (100 mins)
  • Optimizing DNN Performance
  • Incorporating Object Detection

Prerequisites

Familiarity with basic programming fundamentals, like functions and variables
 

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