"deepneural network usually is considered to have at least more than one hidden layer besides the input and output layers usually it consists of minimum of three to four hidden layers deep architectures have multi-layered architecture where each layer consists of multiple non-linear processing units each layer' input is the previous layer in the architecture the first layer is usually the input and the last layer is the output can perform automated feature extractionclassificationanomaly detectionand many other machine learning tasks this should give you good foundational grasp of the concepts pertaining to deep learning suppose we had real-world problem of object recognition from images figure - will give us good idea of how typical machine learning and deep learning pipelines differ (sourceyann lecunfigure - comparing various learning pipelines by yann lecun you can clearly see how deep learning methods involve hierarchical layer representation of features and concept from the raw data as compared to other machine learning methods we conclude this section with brief coverage of some essential concepts pertaining to deep learning