Coursera
Paid Course
English
Paid Certificate Available
2 hours worth of material
selfpaced
Overview
Class Central Tips
In this 1-hour long project-based course, you will be able to:
- Understand the theory and intuition behind Autoencoders
- Import Key libraries, dataset and visualize images
- Perform image normalization, pre-processing, and add random noise to images
- Build an Autoencoder using Keras with Tensorflow 2.0 as a backend
- Compile and fit Autoencoder model to training data
- Assess the performance of trained Autoencoder using various KPIs
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
- Understand the theory and intuition behind Autoencoders
- Import Key libraries, dataset and visualize images
- Perform image normalization, pre-processing, and add random noise to images
- Build an Autoencoder using Keras with Tensorflow 2.0 as a backend
- Compile and fit Autoencoder model to training data
- Assess the performance of trained Autoencoder using various KPIs
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Taught by
Ryan Ahmed
