Introduction to Computer Vision.with TensorFlow

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Introduction to Computer Vision with TensorFlow

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 30M | 498 MB

Genre: eLearning | Language: English

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This course provides an introduction to using deep learning models to solve computer vision tasks in TensorFlow and is focused on the work horse of deep learning image models: the convolutional neural network

 

Expert Lucas Adams teaches you how to get these models up and running fast, especially in domains with limited computing resources or training data, and shows you how to modify the architecture of a neural network to make the model specialized to different tasks. Learners should be familiar with basic deep learning concepts like the multilayer perceptron, linear algebra, Jupyter notebooks, and the basics of building and running TensorFlow programs.

 

Understand why the convolutional neural network works so well for vision tasks

Explore how each component of the architecture contributes to prediction

Learn to run models using weights pre-trained on large datasets using many processing hours

Discover how to modify pre-trained networks for completely different tasks

Tune pre-trained models to a dataset while using knowledge stored from an initial training run

 

 

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