figure - performance comparison of deep learning and traditional machine learning by andrew ng indeedas rightly pointed out by andrew ngthere have been several noticeable trends and characteristics related to deep learning that we have noticed over the past decade they are summarized as follows deep learning algorithms are based on distributed representational learning and they start performing better with more data over time deep learning could be said to be rebranding of neural networksbut there is lot into it compared to traditional neural networks better software frameworks like tensorflowtheanocaffemxnetand kerascoupled with superior hardware have made it possible to build extremely complexmulti-layered deep learning models with huge sizes deep learning has multiple advantages related to automated feature extraction as well as performing supervised learning operationswhich have helped data scientists and engineers solve increasingly complex problems over time the following points describe the salient features of most deep learning algorithmssome of which we will be using in this book hierarchical layered representation of concepts these concepts are also called features in machine learning terminology (data attributesdistributed representational learning of the data happens through multi-layered architecture (unsupervised learningmore complex and high-level features and concepts are derived from simplerlowlevel features