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Learner Reviews & Feedback for TensorFlow for CNNs: Data Augmentation by Coursera Project Network

12 ratings
4 reviews

About the Course

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....
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1 - 4 of 4 Reviews for TensorFlow for CNNs: Data Augmentation

By Diego F B H

Oct 27, 2020

This is an interesting topic, and the presentation gives us examples to perform a data augmentation 👌

By Priscila A B

Nov 22, 2020


By Denizhan E

Feb 6, 2021

Maybe more explanations about libraries and methods used during the project.

By Yaron K

Apr 21, 2021

Pros : Shows how to do image augmentation on stand alone images and as part of a Keras Neural Network model.

Cons: The notebook can't be downloaded from Coursera Rhyme project , and is not included as part of the project resources. The lecturer instructs you to login to your google account if you want the notebook. Since I felt uncomfortable inputting my user+password into a 3rd party system (with all due respect to Coursera) - I chose to printscreen the parts I wanted to save for future reference.