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
Neural Networks Across Space and Time
Fuelling the AI Revolution with Gaming
Tools to Put Deep Learning Models in Production
Machine Intelligence at Google Scale
Interviews
Neural Networks Across Space and Time
What is the focus of your work today?
I’m one of the lead developers of the management layer for Horizon Cloud; VMware’s cloud-hosted virtual desktop product
What’s the motivation for this talk?
Andre Karpathy has described neural networks as "Software 2.0" Even if he's overestimating, machine learning is only going to get more important. Developers wanting to stay ahead should at least gain a basic understanding of what's involved and how it can be used.
