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 Recognition from Local Scale-Invariant Features and HOG by Dalal and their co-authors, Histograms of oriented gradients for human detection), or IF-IDF vectors for text. Because of the depth of the architecture, the learned representations form a hierarchical structure with knowledge learned at various levels. This parameterized, multi-level, computational graph provides a high degree of the representation. The emphasis on shallow and deep algorithms are significantly different in that shallow algorithms are more about feature engineering and selection, while deep learning puts its emphasis on defining the most useful computational graph topology (architecture) and the ways of optimizing parameters/hyperparameters efficiently and correctly for good generalization ability of the learned representations:

Deep learning algorithms are shown to perform better at extracting non-local and global relationships and patterns in the data, compared to relatively shallow learning architectures. Other useful characteristics of learnt abstract representations by deep learning include:

• It tries to explore most of the abundant huge volume of the dataset, even when the data is unlabeled.
• The advantage of continuing to improve as more training data is added.
• Automatic data representation extraction, from unsupervised data or supervised data, distributed and hierarchical, usually best when input space is locally structured; spatial or temporal—for example, images, language, and speech.
• Representation extraction from unsupervised data enables its broad application to different data types, such as image, textural, audio, and so on.
• Relatively simple linear models can work effectively with the knowledge obtained from the more complex and more abstract data representations. This means with the advanced feature extracted, the following learning model can be relatively simple, which may help reduce computational complexity, for example, in the case of linear modeling.
• Relational and semantic knowledge can be obtained at higher levels of abstraction and representation of the raw data (Yoshua Bengio and Yann LeCun, Scaling Learning Algorithms towards AI, 2007, source: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-014-0007-7).
• Deep architectures can be representationally efficient. This sounds contradictory, but its a great benefit because of the distributed representation power by deep learning. 
• The learning capacity of deep learning algorithms is proportional to the size of data, that is, performance increases as the input data increases, whereas, for shallow or traditional learning algorithms, the performance reaches a plateau after a certain amount of data is provided as shown in the following figure, Learning capability of deep learning versus traditional machine learning: