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Fundamentals of Deep Learning for Computer Vision

3 Labs · 30 Credits · 4h 43m

Deep Learning Badge nvidia intro dl

Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities. In this hands-on course, you will learn the basics of deep learning by training and deploying neural networks. On completion, you will be able to solve your own problems with deep learning.

The Quest below is an older version of this course. For the newer more up-to-date version of this course please explore the new DLI Cloud Platfrom

Image Classification with DIGITS

Deep learning enables entirely new solutions by replacing hand-coded instructions with models learned from examples. Train a deep neural network to recognize handwritten digits by:

  • Loading image data to a training environment
  • Choosing and training a network
  • Testing with new data and iterating to improve performance

  • On completion of this Lab, you will be able to assess what data you should be training from.

    Icon  intro introductory Free 1 Hour 30 Minutes

    Object Detection with DIGITS

    Many problems have established deep learning solutions, but sometimes the problem that you want to solve does not. Learn to create custom solutions through the challenge of detecting whale faces from aerial images by:

  • Combining traditional computer vision with deep learning
  • Performing minor “brain surgery” on an existing neural network using the deep learning framework Caffe
  • Harnessing the knowledge of the deep learning community by identifying and using a purpose built network and end-to-end labeled data.

  • Upon completion of this lab, you will be able to solve custom problems with deep learning.

    Icon  intro introductory Free 1 Hour 30 Minutes

    Neural Network Deployment with DIGITS and TensorRT

    Deep learning allows us to map inputs to outputs that are extremely computationally intense. Learn to deploy deep learning to applications that recognize images and detect pedestrians in real time by:

  • Accessing and understanding the files that make up a trained model
  • Building from each function’s unique input and output
  • Optimizing the most computationally intense parts of your application for different performance metrics like throughput and latency

  • Upon completion of this Lab, you will be able to implement deep learning to solve problems in the real world.

    Icon  intro introductory 30 Credits 1 Hour 30 Minutes

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