TensorFlow for CNNs: Image Segmentation

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Coursera Project Network
在此指导 项目中,您将:

Learn the fundamentals of Image Segmentation algorithms

Learn how to build deep learning image segmentation models

Learn how to create a convolutional neural network with Tensorflow

Clock2 hours
Comment Dots英语(English)

This guided project course is part of the "Tensorflow for Convolutional Neural Networks" series, and this series presents material that builds on the second course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners reinforce their skills and build more projects with Tensorflow. In this 2-hour long project-based course, you will learn practically how to build an image segmentation model which is a key topic in image processing and computer vision with real-world applications, and you will create your own image segmentation algorithm with TensorFlow using real data, and you will get a bonus deep learning exercise implemented with Tensorflow. By the end of this project, you will have learned the fundamentals of image segmentation and created a deep learning model with TensorFlow on a real-world dataset. This class is for learners who want to learn how to work with convolutional neural networks and use Python for solving image segmentation tasks 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 practical deep learning project with TensorFlow. Also, this project provides learners with further knowledge about creating and training convolutional neural networks and improves their skills in Tensorflow which helps them in fulfilling their career goals by adding this project to their portfolios.


Deep LearningImage SegmentationArtificial Neural NetworkConvolutional Neural NetworkTensorflow



  1. Introduction and overview of the project

  2. Import Libraries and Download the Dataset

  3. Preprocess Data and View Segmentation Mask

  4. Define the model

  5. Train the model and Visualize Results

  6. Make Visualized predictions






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