Deep Learning Essentials

Advantages over traditional shallow methods

Traditional approaches are often considered shallow machine learning and often require the developer to have some prior knowledge regarding the specific features of input that might be helpful, or how to design effective features. Also, shallow learning often uses only one hidden layer, for example, a single layer feed-forward network. In contrast, deep learning is known as representation learning, which has been shown to perform better at extracting non-local and global relationships or structures in the data. One can supply fairly raw formats of data into the learning system, for example, raw image and text, rather than extracted features on top of images (for example, SIFT by David Lowe's Object ...