This course will take a look at autoencoders and their applications will help you see how autoencoders are used in dimensionality reduction and denoising. You will implement an artificial neural network and an autoencoder using the Keras framework. By the end of this course, you will be able to implement an autoencoder model using convolutional neural networks. This course is part of the Unsupervised Learning path - complete this course to learn how to implement autoencoders.
Previous experience of using Python is required. Prior knowledge of unsupervised learning concepts such as clustering, k-means clustering, DBSCAN algorithm, principal component analysis, and so on, is essential.