Photo by Leiada Krozjhen on Unsplash A cutting-edge unsupervised method for noise removal, dimensionality reduction, anomaly detection, and more All the tutorials about TensorFlow and neural networks I have shared until now have been about supervised learning. This one will be about the Autoenocder which is an unsupervised learning technique. If I want to express it simply, autoencoders reduce the noises from the data by compressing the input data, and encoding and reconstructing the data. That way autoencoders can reduce the dimensionality or the noise of the data and focus on the real focal point of the input data. As you can see from the introduction to the autoencoders here there is more than one process required. First, a model to compress the input data which is the encoder model. Then another model to reconstruct the compressed data that should be as close as the input data which is a decoder model. In this process, it can remove the noise, reduce