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 and drug design, where they have produced results comparable to and in some cases superior to human experts.

Deep learning, in Wikipedia. Retrieved 2/24/2018 https://en.wikipedia.org/wiki/Deep_learning

Position on the Adoption Curve

Presentations about Deep Learning

VC and Machine Learning Explainer @STVcapital Jay Alammar

Visual Intro to Machine Learning and Deep Learning

Machine Learning blogger and entrepreneur, Self-Driving Car Engineer Scholar @nvidia Jessica Yung

Understanding Deep Learning

Developer Advocate at Google Cloud and PMC Chair for Apache Groovy Guillaume Laforge

Machine Intelligence at Google Scale

Developer at Booking.com; Open Source Contributor in DuckDuckGo, GitHub and Pandas Sahil Dua

Tools to Put Deep Learning Models in Production

Interviews

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