What is it about?
This paper comprehensively reviews summaries of relevant studies in deep learning, much of it from prior state-of-the-art techniques. It also discusses the motivations and principles regarding learning algorithms for deep architectures and serves as source of research pool for researchers in the subject area
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Why is it important?
Deep Learning is very pivotal to a broader family of machine learning methods based on the representations of data which makes it easier to learn tasks such as face recognition and one of its potential, is replacing handcrafted features with efficient algorithms inspired by advances in neuroscience.
Perspectives
The field of Deep Machine Learning is very challenging and evolving as one of the powerful machine learning techniques hence requires much attention by researchers in improving its learning processes. Undoubtedly, advancements in Deep Learning will greatly help shape the future of Machine Learning.
Dr. Ben-Bright Benuwa
Data Link Institute
Read the Original
This page is a summary of: A Review of Deep Machine Learning, International Journal of Engineering Research in Africa, June 2016, Trans Tech Publications,
DOI: 10.4028/www.scientific.net/jera.24.124.
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