Deep Learning is a rapidly growing area of machine learning. To learn more, check out our deep learning tutorial. There is also an older version. Which has also been translated into Chinese. We recommend however that you use the new version. Our deep learning tutorial. Will teach you how to apply these algorithms to your own problems.
http://deeplearning.stanford.edu/
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Deep Learning | deeplearning.stanford.edu Reviews
https://deeplearning.stanford.edu
Deep Learning is a rapidly growing area of machine learning. To learn more, check out our deep learning tutorial. There is also an older version. Which has also been translated into Chinese. We recommend however that you use the new version. Our deep learning tutorial. Will teach you how to apply these algorithms to your own problems.
deeplearning.stanford.edu
UFLDL教程 - Ufldl
http://deeplearning.stanford.edu/wiki/index.php/UFLDL教程
Exercise: Implement deep networks for digit classification. Exercise:Learning color features with Sparse Autoencoders. Spatial pyramids / Multiscale. 英文原文作者: Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen. Retrieved from " http:/ deeplearning.stanford.edu/wiki/index.php/UFLDL%E6%95%99%E7%A8%8B. This page was last modified on 10 April 2013, at 00:26. This page has been accessed 436,140 times.
UFLDL Tutorial - Ufldl
http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial
This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems. And complete sections II, III, IV (up to Logistic Regression) first. Gradient checking and advanced optimization. Visualizing a Trained Autoencoder. Sparse Autoencoder Notation Summary. Working with Large Images.
Unsupervised Feature Learning and Deep Learning Tutorial
http://deeplearning.stanford.edu/tutorial
Welcome to the Deep Learning Tutorial! This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems. Material contributed by: Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen, Adam Coates, Andrew Maas, Awni Hannun, Brody Huval, Tao Wang, Sameep Tandon.
实现主成分分析和白化 - Ufldl
http://deeplearning.stanford.edu/wiki/index.php/实现主成分分析和白化
译注 参见PCA一章中 对图像数据应用PCA算法 一节。 Avg = mean(x, 1); % 分别为每个图像块计算像素强度的均值。 X = x - repmat(avg, size(x, 1), 1);. 如果你在Matlab中实现 或者在C , Java等中实现，但可以使用高效的线性代数库 ，直接求和效率很低。 Sigma = x * x' / size(x, 2);. U,S,V] = svd(sigma);. 的特征向量 一个特征向量一列，从主向量开始排序 ，矩阵S 对角线上的元素将包含对应的特征值 同样降序排列。 XRot = U' * x; % 数据旋转后的结果。 XTilde = U(:,1:k)' * x; % 数据降维后的结果，这里k希望保留的特征向量的数目。 XPCAwhite = diag(1./sqrt(diag(S) epsilon) * U' * x;. XZCAwhite = U * diag(1./sqrt(diag(S) epsilon) * U' * x;. 主成分分析 Principal Components Analysis (PCA).
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| Deep learning
http://blog.souhaib-bentaieb.com/deep-learning
Skip to Primary Content. Research notes and reading lists on various topics by Souhaib Ben Taieb. Written by Souhaib Ben Taieb on November 10, 2013 in Uncategorized. X2190; older posts. Newer posts →. Reading group on Deep Learning. Learning Deep Architectures for AI. Tutorial on Deep Learning Architectures. Dropout: A simple and effective way to improve neural networks. Papers : http:/ arxiv.org/abs/1302.4389 and http:/ jmlr.org/proceedings/papers/v28/wang13a.pdf. Deep Learning at Montreal.
Cynthia O'Donnell – Page 2 – A Data Driven Citizen
http://www.cynthiaodonnell.com/page/2
A Data Driven Citizen. May 27, 2015. June 21, 2015. The very basics of MongoDB and PyMongo:. From pymongo import MongoClient import pprint client = MongoClient('mongodb:/ localhost:{port}') my python dict = { "thing": { "name": "Buggy Bug", "color": "purple", "size": "ginormous" }, "other thing": { "name": "Space Bug", "color": "green", "size": "itsy-bitsy" } } db = client.examples db.things.insert(my python dict) for a in db.things.find(): pprint.pprint a. MongoDB will find a record matching a query eve...
IceTea Tonic: Typescript, a software engineering issue
http://it-tonic.blogspot.com/2013/01/typescript-software-engineering-issue.html
Everything and nothing in particular about Modeling, Architecture, Software engineering,. Saturday, January 12, 2013. Typescript, a software engineering issue. Of course it will be needed to consolidate the software engineering stuff, but if we look after the framework part (which consists in brief to take into account security, form validation, database access, communication with server, UI design, history management, i18n management, and so on.) we could propose the following for start :.
IceTea Tonic: Amber (formerly JTalk)
http://it-tonic.blogspot.com/2012/12/amber-formerly-jtalk.html
Everything and nothing in particular about Modeling, Architecture, Software engineering,. Saturday, December 8, 2012. Amber is a smalltalk engine and ide generating javascript for web development purpose. The IDE is embedded into the browser itself and the compiler is written in javascript upon jquery. So why not thinking of Smalltalk again? I let you discover it by taking a glance to Amber. What would we want more in Amber from now? Some tools we could find in Cloud9. Subscribe to: Post Comments (Atom).
IceTea Tonic: TypeScript
http://it-tonic.blogspot.com/2012/11/typescript.html
Everything and nothing in particular about Modeling, Architecture, Software engineering,. Saturday, November 10, 2012. One month ago, Microsoft presented TypeScript. An open source project under the rules of arts, mimicking even what Google does while presentating the language or even in naming the web site. Why is it the good choice? We also may envisage to port some nice dart libraries into typescript as Dart is also open source. I also would like to have an eclipse plugin or IntelliJ idea for Dart...
IceTea Tonic: GWT roadmap
http://it-tonic.blogspot.com/2013/05/gwt-roadmap.html
Everything and nothing in particular about Modeling, Architecture, Software engineering,. Wednesday, May 22, 2013. There were several nice sessions ar Google I/O, and especially for my own interest Go, cloud and GWT. There was more especially a session about the future of GWT, done by Daniel Kurka, Ray Cromwell. You may find the video here. To summurize, we can say that GWT future is ensured from now, and having two talks at Google I/O two years after Google left officially the project is a good news.
IceTea Tonic: Data Intelligence Review
http://it-tonic.blogspot.com/2013/12/data-intelligence-review.html
Everything and nothing in particular about Modeling, Architecture, Software engineering,. Saturday, December 21, 2013. Just a little post to indicate I've just launched a paper.li curation site about competitive intelligence in the BigData field I've called Data Intelligence Review. Subscribe to: Post Comments (Atom). View my complete profile. Simple template. Powered by Blogger.
IceTea Tonic: Typescript : useful entry points
http://it-tonic.blogspot.com/2013/02/typescript-useful-entry-points.html
Everything and nothing in particular about Modeling, Architecture, Software engineering,. Sunday, February 3, 2013. Typescript : useful entry points. The typescript community is not very widespread until now, but some interesting initiatives make the growing of this community easier. Here are few entry points to search and find features you need :. Where you may find many, many .d.ts files (wrappers around js libraries). A sister project of DefintelyTyped offering a tool to get simply .d.ts files. View m...
IceTea Tonic: Apache Spark platform
http://it-tonic.blogspot.com/2015/04/apache-spark-plateform.html
Everything and nothing in particular about Modeling, Architecture, Software engineering,. Saturday, April 11, 2015. I've not been here on the blog for a while :-( ; many things to do, many things to learn, . In these two years, many things have deeply changed (BigData, IoT, API oriented architecture, machine learning, docker .) and some have been confirmed (typescript, go, .). ETL-like approach for pipelining data processes through Spark Stream. Standardized access to machine learning algorithms(MLLIB) b...
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Deep Learning | Finding Meaning in the Patterns of Life
Finding Meaning in the Patterns of Life. If your gods are Alan Turing, John Carmack, and Gordon Moore, have designed your own processor, taped it out, and written a chess game for it, come join us. Please send resumes to adventure@deeplearning.com. Your Message Has Been Sent! Thank you for contacting us. Oops, An error has ocurred! See the marked fields above to fix the errors. Deep Learning Corporation is a 4Catalyzer Company. Deep Learning Corporation 2014.
11-785 DEEP LEARNING
1:30 pm - 2:50 pm. Http:/ deeplearning.cs.cmu.edu. Contact: email:bhiksha@cs.cmu.edu, Phone:8-9826, Office: GHC6705. Office hours: 3.30-5.00 Mondays. You may also meet me at other times if I'm free. TA: Zhenzhong (Danny) Lan, Volkan Cirik. The labs will exercise the basics of several aspects of implementation and investigation of these networks. Bain on Neural Networks. Brain and Cognition 33:295-305, 1997. Alan L. Wilkes and Nicholas J. Wade. WS McCulloch and W.H. Pitts. The First Computational Theory o...
Home - Toronto Deep Learning Demos
Toronto Deep Learning Demos. Enter an image URL. Example Images: click to classify and retrieve! Recent Searches: No Records. Recent searches require Cookies Enabled. Please help us improve results by clicking on check. Live demo of Deep Learning technologies from the Toronto Deep Learning group. Server and website created by Yichuan Tang. Deeplearning at cs . toronto . edu.
Deep Learning | Heading for Real AI
Heading for Real AI. Welcome to Deep Learning Japan! Deep Learning は機械学習アルゴリズムの１つで, 人間の脳を模した構造をもつニューラルネットワークを多層に重ねた構造をもちます. Deep Learning の大きな特徴は, 多段に重ねることによって抽象的なテ ータの表現を獲得することができる点で, 真の人工知能への第一歩であると考えられます. すでに海外では盛んに研究されていますが, 知識の不足や実装面の難しさから研究をはじめるのが難しい状況です. 当研究室では週１回の研究ミーティングと研究活動を通して以下のような知見の蓄積を試みています. 本ページでは, 実装方法, 最新研究に関するサーベイ資料などを公開する予定です. Pylearn2 torch7 AWS Image (AMI). 人工知能学会チュートリアル Deep Learning 資料更新しました. Theme: Base WP by Iografica Themes.
Deep Learning
Deep Learning Research Groups. ICML 2013 Challenges in Representation Learning. Deep Learning Job Listings. 8230; moving beyond shallow machine learning since 2006! MILA is Hiring Two Software Engineers. OpenAI: A new non-profit AI company. Conference on the Economics of Machine Intelligence-Dec 15. Open Discussion of ICLR 2016 Papers is Now Open. Software Developer Position at MILA. Welcome to Deep Learning. A list of deep learning research groups and labs,. As well as tutorials. Discuss on our WP Forum.
Deep Learning
Deep Learning is a rapidly growing area of machine learning. To learn more, check out our deep learning tutorial. There is also an older version. Which has also been translated into Chinese. We recommend however that you use the new version. Our deep learning tutorial. Will teach you how to apply these algorithms to your own problems.
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Deeplearning4j: Open-source, Distributed Deep Learning for the JVM
Get a free preview of Deep Learning: A Practitioner's Approach. Deep Learning for Java. Open-Source, Distributed, Deep Learning Library for the JVM. Download SKIL Community Edition. Quick Reference: Layers and Functionality. Build Locally From Master. Use the Maven Build Tool. Or Configure DL4J in Ivy, Gradle, SBT etc. Swap CPUs for GPUs. Machine learning server API docs. Deep Learning Tutorial Index. Using Recurrent Nets in DL4J. Use ND4J for Scientific Computing. Build a Recommendation Engine With DL4J.
Deeplearning4java.com
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Deeplearning4java.org
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