Introduction to Deep Learning

Traditional machine learning models have always been very powerful to handle structured data and have been widely used by businesses for credit scoring, churn prediction, consumer targeting, and so on. The success of these models highly depends on the performance of the feature engineering phase: the more we work close to the business to extract relevant knowledge from the structured data, the more powerful the model will be. When it comes to unstructured data (images, text, voice, videos), hand engineered features are time consuming, brittle and not scalable in practice. That is why Neural Networks become more and more popular thanks to their ability to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task.