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.
http://deeplearning.cs.toronto.edu/
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Home - Toronto Deep Learning Demos | deeplearning.cs.toronto.edu Reviews
https://deeplearning.cs.toronto.edu
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.
deeplearning.cs.toronto.edu
Resources - Toronto Deep Learning
http://deeplearning.cs.toronto.edu/resources
Toronto Deep Learning Demos. Metacademy - Deep Learning. Multimodal Learning with Deep Boltzmann Machines. Multimodal Neural Language Models. Multi-task Neural Networks for QSAR Predictions. Winner of Merck Challenge. Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models. Live demo of Deep Learning technologies from the Toronto Deep Learning group. Server and website created by Yichuan Tang. Deeplearning at cs . toronto . edu.
People - Toronto Deep Learning
http://deeplearning.cs.toronto.edu/people
Toronto Deep Learning Demos. For a complete list of people in deep learning and machine learning, please see Machine Learning - University of Toronto. Geoff is interested in ways of getting deep neural networks to learn good representations for objects, sounds, and strings of words. He splits his time between Google and the University of Toronto and spends a few months each year in Mountain View. Charlie's research interests include machine learning, computer vision and cognitive science. Specificall...
Source code - Toronto Deep Learning
http://deeplearning.cs.toronto.edu/codes
Toronto Deep Learning Demos. ConvNet: A GPU implementation of Convolutional Neural Nets in C. Deep Belief Networks and Deep Boltzmann Machines. Bayesian Probabilistic Matrix Factorization. GPU based Deep Learning Models. Convolutional Neural Net for Expression classification. CUDA with Matlab interface. Multimodal Neural Language Models. Implementation of the paper "Multimodal Neural Language Models" (Kiros et al. ICML14). Scripts to evaluate models, perform retrieval and generations.
Images to Text - Toronto Deep Learning
http://deeplearning.cs.toronto.edu/i2t
Toronto Deep Learning Demos. Due to heavy traffic, server might be slow. Also try the Static Demo. Enter an image URL. Example Images: click to classify and retrieve! Recent Searches: No Records. Recent searches require Cookies Enabled. May Take a Few Seconds. Live demo of Deep Learning technologies from the Toronto Deep Learning group. Server and website created by Yichuan Tang. Deeplearning at cs . toronto . edu.
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UofT Machine Learning | Toronto Deep Learning Projects
http://learning.cs.toronto.edu/news/toronto-deep-learning-projects
Toronto Deep Learning Projects. Check out the exciting deep learning research. In our group and the new website for deep learning projects! Http:/ deeplearning.cs.toronto.edu/.
spring 2015 | rebecca's WIP [at] ITP
http://www.peachriot.com/category/spring-2015
Rebecca's WIP [at] ITP. ViewMaster Final Project Documentation. After many weeks post-Spring Show, I finally have pulled together solid documentation for my final Readymades project,. What in the world do you want to see? WHAT IN THE WORLD DO YOU WANT TO SEE? What if you were able to travel to an office in Moscow or a parking lot in Nebraska? To watch people sitting poolside, folding their laundry, or eating lunch in the food court of a mall? What in the world do you want to see? For my final in Reading ...
reading + writing electronic text | rebecca's WIP [at] ITP
http://www.peachriot.com/category/spring-2015/rwet
Rebecca's WIP [at] ITP. Reading writing electronic text. RWET Final Project: It Sees. Reading writing electronic text. An example from Visually Similar Image Processing, which I made in Fall 2014. My final project in Reading and Writing Electronic Text is part of a larger exploration of image recognition, image-text translation and the poetic possibilities in machine vision. I started this exploration back in the fall with my Intro to Computational Media final project. As the engine to generate my image-...
INTERESTING.JPG
http://www.cmyr.net/work/interesting-jpg.html
Index of cmyr.net. INTERESTING.JPG explores the capabilities and limitations of the current state-of-the-art in image analysis and feature detection. Working with a model developed by the University of Toronto’s Deep Learning Group. It attempts to generate captions for images of breaking news events. It automatically posts the images and their captions to a twitter account. And source is available on github. A bunch of men trying to climb a tree . The two men are talking to each other on the sidewalk .
UofT Machine Learning | Projects
http://learning.cs.toronto.edu/category/projects
U of T Machine Learning group spins out company Deep Genomics. Aug 5th, 15. Checkout http:/ www.deepgenomics.com/. Reconstructing the evolutionary history of tumors. Feb 19th, 15. Phylo* is a family of statistical methods that use nonparametric Bayesian tree priors to infer clonal evolution of tumors from whole genome sequencing data. Http:/ www.worldscientific.com/doi/abs/10.1142/9789814644730 0004. Http:/ www.biomedcentral.com/1471-2105/15/35. Deep Learning Discovers Genetic Causes of Diseases. My new,...
UofT Machine Learning | News
http://learning.cs.toronto.edu/category/news
U of T Machine Learning group spins out company Deep Genomics. Aug 5th, 15. Checkout http:/ www.deepgenomics.com/. 7 papers accepted by ICML 2015. May 8th, 15. Reconstructing the evolutionary history of tumors. Feb 19th, 15. Phylo* is a family of statistical methods that use nonparametric Bayesian tree priors to infer clonal evolution of tumors from whole genome sequencing data. Http:/ www.worldscientific.com/doi/abs/10.1142/9789814644730 0004. Http:/ www.biomedcentral.com/1471-2105/15/35. Jan 6th, 15.
RWET Final Project: It Sees | rebecca's WIP [at] ITP
http://www.peachriot.com/rwet-final-project-it-sees
Rebecca's WIP [at] ITP. Reading writing electronic text. RWET Final Project: It Sees. An example from Visually Similar Image Processing, which I made in Fall 2014. My final project in Reading and Writing Electronic Text is part of a larger exploration of image recognition, image-text translation and the poetic possibilities in machine vision. I started this exploration back in the fall with my Intro to Computational Media final project. What happens when you translate an image to text? To scrape the site...
@dronesweetie: My first Twitter bot! | rebecca's WIP [at] ITP
http://www.peachriot.com/dronesweetie-my-first-twitter-bot
Rebecca's WIP [at] ITP. Reading writing electronic text. Dronesweetie: My first Twitter bot! It’s a bird, it’s a plane…it’s images of drones, as described by a computer. I recently made my very first Twitter bot based on the notes from Allison’s How to Make a Twitter Bot workshop. Now I can’t stop thinking of ideas for bots. The name of my inaugural bot is @dronesweetie. Dronesweetie is a Twitter bot that tweets computer-generated descriptions of images of drones. Th. Below is a list of some of my output...
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Deep Learning
Deep Learning
Jimmy Ren's Home Page
Jimmy SJ. Ren 任思捷. Email: jimmy.sj.ren@gmail.com. I am currently a research scientist at SenseTime. I finished my Ph.D from City University of Hong Kong. In 2013. My research interests are: machine learning and deep learning with focus on deep convolutional neural networks, recurrent neural networks and their applications in computer vision, image processing, NLP and business. I am also interested in parallel computing on GPUs and Agile software development. See my one page. Find me on facebook. Wei Wang...
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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