Displaying 1-16 of 86 results for: deep learning
Deep Learning - The Biggest Data Science Breakthrough of the Decade - ...
By Jeremy Howard
Publish Date: April 04, 2014
Machine learning and AI have appeared on the front page of the New York Times three times in recent memory: 1) When a computer beat the world's #1 chess player 2) When Watson beat the world's best Jeopardy players 3) When deep learning algorithms won...
How to Get Started with Deep Learning in Computer Vision - O'Reilly Media ...
By Pete Warden
Publish Date: June 01, 2015
Hosted By: Ben Lorica Watch the webcast recording There have been big improvements in image analysis over the last few years thanks to the adoption of deep learning neural networks to solve vision problems, but figuring out how to get ...
Introduction to Parallel Iterative Deep Learning on Hadoop’s Next -Generation...
By Josh Patterson, Adam Gibson
Publish Date: July 20, 2014
In this session, we will take a look at how we parallelize Deep Belief Networks in Deep Learning on the next -generation YARN framework Iterative Reduce and the parallel machine learning library Metronome. We’ll also take a look at some real world applications of Deep Learning on Hadoop such as image classification and NLP.
Deep learning made doubly easy with reusable deep features : Big Data ...
By Carlos Guestrin
Publish Date: May 05, 2015
Deep learning is a promising machine learning technique with a high barrier to entry. In this talk, we provide an easy entry into this field via "deep features" from pre-trained models. These features can be trained on one data set for one task and used to obtain good predictions on a different task, on a different data set. No prior experience is necessary.
Mocha.jl - Deep learning for Julia: Open Source Convention - O'Reilly OSCON, ...
By Chiyuan Zhang
Publish Date: July 20, 2015
Mocha.jl is an efficient and flexible deep learning framework for Julia. It supports multiple computation backends, leading to 20~30 times faster training on a modern GPU device. We will use an example to illustrate the user interfaces of Mocha.jl and also introduce the design and architecture behind the library implementations.
Deep Learning oral traditions - O'Reilly Radar
By Ben Lorica
Publish Date: October 20, 2013
This past week I had the good fortune of attending two great talks1 on Deep Learning, given by Googlers Ilya Sutskever and Jeff Dean. Much of the excitement surrounding...
Deep Learning and the Dream of AI: Strata Conference + Hadoop World 2013 - O ...
By Brandon Ballinger
Publish Date: October 28, 2013
Deep learning has upset the best results in speech recognition, computer vision, and other fields. How do deep neural nets work? What makes them different than the classical neural nets of the 70's? How is deep learning getting us closer to the original dream of AI -- machines that can think?
Beyond DNNs towards New Architectures for Deep Learning, with Applications to...
By Tara Sainath
Publish Date: February 17, 2015
DNNs were first explored for acoustic modeling, where numerous research labs demonstrated improvements in WER between 10-40% relative. In this talk, I will provide an overview of the latest improvements in deep learning across various research labs since the initial inception.
How to build and run your first deep learning network - O'Reilly Radar
By Pete Warden
Publish Date: July 23, 2014
When I first became interested in using deep learning for computer vision I found it hard to get started. There were only a couple of open source projects...
Building Machine Learning Systems with Python, 2nd Edition
By Luis Pedro Coelho, Willi Richert
Publisher: Packt Publishing
Release Date: March 2015
Learning Apache Kafka, 2nd Edition
By Nishant Garg
Publisher: Packt Publishing
Release Date: February 2015
What is deep learning, and why should you care? - O'Reilly Radar
By Pete Warden
Publish Date: July 14, 2014
Editor's note: this post is part of our Intelligence Matters investigation. When I first ran across the results in the Kaggle image-recognition competitions, I didn't believe them. I've...