TensorFlow for CNNs: Data Augmentation

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Coursera Project Network

Learn how to apply Data Augmentation on Images

Learn how to artificially increase the number of training examples

Learn how to create Data Augmentation models with Keras and 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 In this project, you will learn practically how to build a data augmentation model which is a key topic in training visual recognition systems with real-world applications, and you will create your own data augmentation algorithm with TensorFlow and apply it to 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 data augmentation and created a deep learning model with TensorFlow, and applied data augmentation using real images. This class is for learners who want to learn how to work with convolutional neural networks and use Python for applying data augmentation to images 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.


TensorflowConvolutional Neural NetworkArtificial Neural NetworkDeep Learning



  1. Introduction and overview of the project

  2. Import Libraries and Setup the Dataset

  3. Use Keras Layers for Rescaling, Augmentation

  4. Use Keras for Preprocessing and Training

  5. Use Tensorflow to Apply Image Augmentation






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