TensorFlow for AI: Applying Image Convolution

Coursera Project Network

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

Clock1.5 hours
Comment Dots英语(English)

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.


Deep LearningConvolutional Neural NetworkMachine LearningPython ProgrammingTensorflow



  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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