Displaying 1-16 of 86 results for: deep learning

Displaying 1-16 of 86 results for: deep learning

Deep Learning

By Adam Gibson, Josh Patterson

Publisher: O‘Reilly Media

Release Date: September 2015

Deep Learning

By O‘Reilly Media, Inc.

Publisher: O‘Reilly Media

Release Date: June 16, 2015

Fundamentals of Deep Learning

By Nikhil Buduma

Publisher: O‘Reilly Media

Release Date: June 2015

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

Machine Learning

By Jason Bell

Publisher: Wiley

Release Date: October 2014

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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...

时间: 2024-08-04 18:24:07

Displaying 1-16 of 86 results for: deep learning的相关文章

16 On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima 1609.04836v1

Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang Northwestern University & Intel code: https://github.com/keskarnitish/large-batch-training * SGD及其变种在batch size增大的时候会有泛化能力的明显下降 generalization drop/deg

《Deep Learning》(深度学习)中文版 开发下载

<Deep Learning>(深度学习)中文版开放下载   <Deep Learning>(深度学习)是一本皆在帮助学生和从业人员进入机器学习领域的教科书,以开源的形式免费在网络上提供, 这本书是由学界领军人物 Ian Goodfellow.Yoshua Bengio 和 Aaron Courville 合力打造. 书籍原版英文目录: Deep Learning Table of Contents Acknowledgements Notation 1 Introduction

2016.4.15 nature deep learning review[1]

今天,我本来想膜一下,所以找到了上古时期发表再nature上的反向传播的论文,但是没看下去...所以,翻出来了15年发表在nature上的deep learning,相当于一个review,来阅读一下,而且感觉引文会比较重要,所以这篇中枢值较高的文献拿来学一学. 相关资料: 英文原文: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.436.894&rep=rep1&type=pdf 中文翻译: http://www.csd

How to Grid Search Hyperparameters for Deep Learning Models in Python With Keras

Hyperparameter optimization is a big part of deep learning. The reason is that neural networks are notoriously difficult to configure and there are a lot of parameters that need to be set. On top of that, individual models can be very slow to train.

【CS-4476-project 6】Deep Learning

AlexNet / VGG-F network visualized by mNeuron. Project 6: Deep LearningIntroduction to Computer Vision Brief Due date: Tuesday, December 6th, 11:55pm Project materials including starter code, training and testing data, and html writeup template: proj

Why Deep Learning Works – Key Insights and Saddle Points

Why Deep Learning Works – Key Insights and Saddle Points A quality discussion on the theoretical motivations for deep learning, including distributed representation, deep architecture, and the easily escapable saddle point. By Matthew Mayo. This post

Transfer learning &amp; The art of using Pre-trained Models in Deep Learning

原文网址:  https://www.analyticsvidhya.com/blog/2017/06/transfer-learning-the-art-of-fine-tuning-a-pre-trained-model/ Introduction Neural networks are a different breed of models compared to the supervised machine learning algorithms. Why do I say so? Th

Udacity Nanodegree Program: Deep Learning Foundation: New Syllusbus

Program Structure Every week, you can expect to see this content coming up: Siraj's introductory video & One hour coding session Additional lesson(s) from Mat & other Udacity experts Then, approximately every four weeks you'll get a project. The f

(转)分布式深度学习系统构建 简介 Distributed Deep Learning

HOME ABOUT CONTACT SUBSCRIBE VIA RSS DEEP LEARNING FOR ENTERPRISE Distributed Deep Learning, Part 1: An Introduction to Distributed Training of Neural Networks Oct 3, 2016 3:00:00 AM / by Alex Black and Vyacheslav Kokorin Tweet inShare27   This post