Introduction to Deep Learning/Neural Networks

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The next talk will be about Deep Learning which will be good preparation for the next hands on session which will be a 3 hour Saturday session in a few weeks.

Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a class of machine learning (https://en.wikipedia.org/wiki/Machine_learning) algorithms (https://en.wikipedia.org/wiki/Algorithm) that:

Use a cascade of many layers of nonlinear processing (https://en.wikipedia.org/wiki/Nonlinear_filter) units for feature extraction (https://en.wikipedia.org/wiki/Feature_extraction) and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised (https://en.wikipedia.org/wiki/Supervised_learning) or unsupervised (https://en.wikipedia.org/wiki/Unsupervised_learning) and applications include pattern analysis (unsupervised) and classification (supervised). Are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher level features are derived from lower level features to form a hierarchical representation.

Are part of the broader machine learning field of learning representations of data.

Learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts.

See: https://en.wikipedia.org/wiki/Deep_learning