Getting Started

Deep learning also called deep machine learning, or deep structured learning, or hierarchical learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures, or otherwise composed of multiple non-linear transformations.

Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape,etc.. Some representations make it easier to learn tasks (e.g., face recognition or facial expression recognition[6]) from examples. 

One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction. Deep learning is used in the research community and in industry to help solve many big data problems such as computer vision, speech recognition, and natural language processing. Practical examples include:

  • Vehicle, pedestrian and landmark identification for driver assistance
  • Image recognition
  • Speech recognition and translation
  • Natural language processing
  • Life sciences