Applied Deep Learning - Part 3: Autoencoders – Towards Data Science

Welcome to Part 3 of Applied Deep Learning series. Part 1 was a hands-on introduction to Artificial Neural Networks, covering both the theory and application with a lot of code examples and visualization. In Part 2 we applied deep learning to real-world datasets, covering the 3 most commonly encountered problems as case studies: binary classification, multiclass classification and regression. Now we will start diving into specific deep learning architectures, starting with the simplest: Autoencoders. The code for this article is available here as a Jupyter notebook, feel free to download and try it out yourself.