TensorFlow for AI: Applying Image Convolution

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提供方
在此指导项目中,您将:

Learn how to create convolution and pooling layers for images

Learn how to apply filters to images and detect edges

Learn how to build convolutional layers for neural networks

1.5 hours
中级
无需下载
分屏视频
英语(English)
仅限桌面

This guided project course is part of the "Tensorflow for AI" series, and this series presents material that builds on the first course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners reinforce their skills and build more projects with Tensorflow. In this 1.5-hour long project-based course, you will discover convolutions, apply filters to images, apply pooling layers, and try out the convolution and pooling techniques on real images to learn about how convolutions work. At the end of the project, you will get a bonus deep learning project implemented with Tensorflow. By the end of this project, you will have learned how convolutions work and how to create convolutional layers to prepare for your own deep learning projects using convolutional neural networks. This class is for learners who want to use Python for building convolutional neural networks with TensorFlow, and for learners who are currently taking a basic deep learning course or have already finished a deep learning course and are searching for a knowledge-based course about convolutions in images with TensorFlow. Also, this project provides learners with needed knowledge about building convolutional neural networks and improves their skills in applying filters to images which helps them in fulfilling their career goals by adding this project to their portfolios.

您要培养的技能

  • Deep Learning

  • Convolutional Neural Network

  • Machine Learning

  • Python Programming

  • Tensorflow

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Introduction and overview of the project

  2. Definition and understanding of convolutions

  3. Draw the image, store it and apply convolutions

  4. Create visualized filters and convolutions

  5. Apply convolutions and pooling to Images

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