An introduction to Autoencoders for Beginners

Autoencoders are unstructured learning models that utilize the power of neural networks to perform the task of representation learning. In the context of machine learning, representation learning means embedding the components and features of original data in some low-dimensional structure for better understanding, visualizing, and extraction of meaningful information. These low dimensional vectors can help us gain amazing information about our data such as how close two instances of the dataset are, finding structure and patterns in the dataset, etc. In this big-data era, where petabytes of data are generated and processed by leading social networking sites and e-commerce giants, we are living in a world of data abundance. Our machine learning algorithms have only mainly exploited labeled datasets which are rare and costly. Most of the data generated are unstructured and unlabelled, so it is high time our machine learning community should focus on unsupervised learning algorithms and not just the supervised ones to unlock the true potential of AI and machine learning.