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.
Source: https://en.wikipedia.org/wiki/Deep_learning
Position on the Adoption Curve
Presentations about Deep Learning
Tackling Computing Challenges @CERN
Getting Started in Deep Learning with TensorFlow 2.0
From Research to Production With PyTorch
Interviews
From Research to Production With PyTorch
What is the focus of your work today?
I work on PyTorch, which is an open source deep Learning framework developed here at Facebook. I specifically work with the team on a lot of the ways in which we enable the success of a broad open source community that spans both bleeding edge researchers as well as ML product engineers putting deep learning technology to use in products deployed today. I work a lot with trying to understand how we can improve that tool chain, serve unmet needs and unlock the ability to build things which have never been built before using this technology.
What is the motivation for this particular talk?
The focus of this talk is on the journey from research to production for deep learning models. This is a long running challenge within the field of machine learning. Many machine learning techniques originate in academia without the requirement to use those techniques in the real world. As we've been able to make major advances in the field of machine learning, particularly in deep learning, we as ML engineers have found ways to incorporate these techniques into useful real-world products.
