Fundamentals of Deep Learning for Computer Vision Eğitimi

  • Eğitim Tipi: Classroom / Virtual Classroom / Online
  • Süre: 1 Gün
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This workshop teaches deep learning techniques for a range of computer vision tasks through a series of hands-on exercises. You will work with widely-used deep learning tools, frameworks, and workflows to train and deploy neural network models on a fully-configured, GPU-accelerated workstation in the
cloud. After a quick introduction to deep learning, you will advance to: building and deploying deep learning applications for image classification and object detection, modifying your neural networks to improve their accuracy and performance, and implementing the workflow 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.

  • Familiarity with programming fundamentals such as functions and variables

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
  • Gain real-world expertise through content designed in collaboration with industry leaders including the Children’s Hospital Los Angeles, Mayo Clinic, and PwC
  • Earn NVIDIA DLI Certification to demonstrate your subject matter competency and support professional career growth
  • Access courses anywhere, anytime with a fully-configured, GPU-accelerated workstation in the cloud
Certification: Upon successful completion of the workshop, participants will receive NVIDIA DLI Certification to recognize subject matter competency and support professional career growth.

  • Course overview
  • Getting started with deep learning
Introduction to deep learning, situations in which it is useful, key terminology, industry trends, and challenges.
Unlocking New Capabilities
  • 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
  • Deploying DNN models
Hands-on exercise: deployment of trained neural networks from their training environment into real applications.
Measuring and Improving Performance
  • Optimizing DNN performance
  • Incorporating object detection
Hands-on exercise: neural network performance optimization and applying DNNs to object detection.
  • Summary of key learnings
  • Review of concepts and practical takeaways
  • Assessment project: train and deploy a deep neural network
  • Validate learnings by applying the deep learning application development workflow (load dataset, train and deploy model) to a new problem.
Next Steps
  • Workshop survey
  • Setting up your own GPU-enabled environment
  • 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
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