Learning Computer Vision with TensorFlow [Video]

Learning Computer Vision with TensorFlow [Video]

Marvin Bertin

Exploit the power of TensorFlow to create powerful image processing applications
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Video Description

TensorFlow has gained immense popularity over the past few months, owing to its power and ease of use. This video aims to help you leverage the power of TensorFlow to do image processing. Beginning with an introduction to image processing, the video will take you through TensorFlow's API-like graph tensor, which is generally used for image classification.

Starting off with basic 2D images, the video will gradually take you through recognizing more complex images, colors, shapes, and so on. Making use of the Python API, you will move on to classifying and training your model to identify objects in an image.

Then you will learn about Convolutional Neural Networks, its architecture and why they perform well in the image takes. You will dive into the different layers available in TensorFlow. Further, we will construct the neural network feature extractor to embed images into a dense and rich vector space and finally perform fine-tuning optimization using pre-trained neural networks.

Style and Approach

This video is a practical guide to implementing TensorFlow in production. It explores various scenarios in which you can use TensorFlow and shows you how to use it in the context of real-world projects. This will not only give you the upper hand in the field but shows the potential for innovative uses of TensorFlow in your environment.

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What You Will Learn

  • Build powerful multi-class image classifiers
  • Build a neural feature extractor that can embed images into a dense and rich vector space.
  • Perform fine-tuning optimization on new predictive tasks using pre-trained neural networks
  • Optimize a neural network with stochastic gradient descent and other advanced optimization methods
  • Build functional model classes and methods with TensorFlow-Keras' Functional API
  • Choose the right loss function and evaluation metric for the right task
  • Build a computational graph representation of a Neural Network
  • Train a neural network with automatic back propagation

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Video Details

ISBN 139781788292573
Course Length2 hours 01 minutes
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