Deep Learning

• Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.

• Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design and board game programs, where they have produced results comparable to and in some cases superior to human experts.

• Deep learning models are vaguely inspired by information processing and communication patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains, which make them incompatible with neuroscience evidences.

         Deep learning is a class of machine learning algorithms that:

• use a cascade of multiple layers of non linear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input.

• learn in supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners.

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

• In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation. In an image recognition application, the raw input may be a matrix of pixels; the first representational layer may abstract the pixels and encode edges; the second layer may compose and encode arrangements of edges; the third layer may encode a nose and eyes; and the fourth layer may recognize that the image contains a face. Importantly, a deep learning process can learn which features to optimally place in which level on its own. (Of course, this does not completely obviate the need for hand-tuning; for example, varying numbers of layers and layer sizes can provide different degrees of abstraction.

• Deep learning algorithms can be applied to unsupervised learning tasks. This is an important benefit because unlabeled data are more abundant than labeled data. Examples of deep structures that can be trained in an unsupervised manner are neural history compressors and deep belief networks.

• Deep learning architectures are often constructed with a greedy layer-by-layer method.Deep learning helps to disentangle these abstractions and pick out which features improve performance.

        

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         Next week I will post more about AI...so stay tuned!