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Drag-and-drop data analytics | MIT News
In the Iron Man movies, Tony Stark uses a holographic computer to project 3-D data into thin air, manipulate them with his hands, and find fixes to his superhero troubles. via Pocket
analysis  deep  interactive  ml  tools 
9 days ago by kintopp
Visualizing Image Fields
Experimental gesture atlas built using Posenet.
art  deep  interactive  pose  visualization 
20 days ago by kintopp
kraken — kraken 2.0.5-4-gbb42ba5 documentation
kraken is a turn-key OCR system forked from ocropus. It is intended to rectify a number of issues while preserving (mostly) functional equivalence. If you already got a model trained for ocropus you can always expect it to work with kraken without all the fuss of the original ocropus tools. via Pocket
deep  ocr  tools 
27 days ago by kintopp
Nicolas Gonthier
Je suis doctorant au sein de l’équipe IMAGES du LTCI de Télécom ParisTech. Mes intérêts de recherche sont l’apprentissage profond (Deep Learning) appliqué à l’histoire de l’art et aux données historiques en générale.
art  arthistory  france  images  history  analysis  deep 
27 days ago by kintopp
Postdoc Job – Apply Now!
We’re hiring a postdoc in image processing and classification! Job Description DataLab is inviting applications for a full-time postdoctoral scholar in image processing and classification beginning Summer/Fall 2019. via Pocket
analysis  arthistory  deep  images  jobs 
27 days ago by kintopp
Summer School: Machine Learning for Language Analysis
The “Summer School on Deep Learning for Language Analysis” addresses students and doctoral candidates from linguistics and digital humanities, as well as other fields that are involved with machine learning techniques. via Pocket
deep  germany  learn  text 
5 weeks ago by kintopp
KB College: AI en de Bibliotheek - de computer leest alles | Koninklijke Bibliotheek
Je zou denken dat deze begrippen niet verder uit elkaar kunnen liggen: de Bibliotheek en Artificial Intelligence (AI). De Bibliotheek is een eeuwenoud en betrouwbaar instituut. AI is een nieuwe technologie die zich razendsnel ontwikkelt, met veel onzekerheid over de kansen en risico’s. via Pocket
conference  deep  diglib  libraries  netherlands  ml 
7 weeks ago by kintopp
Is Ethical A.I. Even Possible? - The New York Times
HALF MOON BAY, Calif. — When a news article revealed that Clarifai was working with the Pentagon and some employees questioned the ethics of building artificial intelligence that analyzed video captured by drones, the company said the project would save the lives of civilians and soldiers. via Pocket
deep  ethics  ml 
8 weeks ago by kintopp
Interpretable Machine Learning
Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. via Pocket
analysis  deep  methodology  ml 
8 weeks ago by kintopp
AI, Ethics And Society –
Last week we held a conference on AI, Ethics and Society at the University of Alberta. As I often do, I kept conference notes at: : AI Ethics And Society. The conference was opened by Reuben Quinn whose grandfather signed Treaty 6. via Pocket
canada  conference  deep  ethics  ml 
8 weeks ago by kintopp
Visualization in Deep Learning - Multiple Views: Visualization Research Explained - Medium
TL;DR: The democratization of AI is either near or already here—the barrier to developing and deploying neural networks is lower than ever before. But complex deep learning models are hard to train and hard to understand. via Pocket
deep  interface  methodology  visualization 
8 weeks ago by kintopp
Our goal in this paper is to discover near duplicate patterns in large collections of artworks. This is harder than standard instance mining due to differences in the artistic media (oil, pastel, drawing, etc), and imperfections inherent in the copying process. via Pocket
art  arthistory  iconography  images  recognition  search  deep 
march 2019 by kintopp
Applying Neural Networks Webinar Series: Wolfram U
Take a deep dive into the latest workflows for building, training and evaluating neural networks. See how you can use custom neural net models to solve complex processing tasks involving image, audio and natural language data. via Pocket
learn  wolfram  deep 
march 2019 by kintopp
iArt: Ein interaktives Analyse- und Retrieval-Tool zur Unterstützung von bildorientierten Forschungsprozessen
analysis  art  arthistory  germany  images  munich  ml  deep 
february 2019 by kintopp
Automatic art analysis has been mostly focused on classifying artworks into different artistic styles. However, understanding an artistic representation involves more complex processes, such as identifying the elements in the scene or recognizing author influences. via Pocket
analysis  art  paper  semantic  deep 
november 2018 by kintopp
DCGAN for Archaeologists – Electric Archaeology
Melvin Wevers has been using neural networks to understand visual patterns in the evolution of newspaper advertisements in Holland. He and his team developed a tool for visually searching the newspaper corpus. via Pocket
images  newspapers  recognition  deep 
november 2018 by kintopp
Introduction to Local Interpretable Model-Agnostic Explanations (LIME) - O'Reilly Media
Check out the Data science and machine learning sessions at Strata Data in New York, September 25-28, 2017, for more on current trends and practical use cases in applied data science. Machine learning is at the core of many recent advances in science and technology. via Pocket
methodology  models  ml  deep 
august 2018 by kintopp
Where Computer Vision Meets Art
4th Workshop on Computer Vision for Art Analysis
9th September 2018, Munich, Germany
analysis  art  arthistory  cfp  conference  deep  germany  images  recognition  ml 
july 2018 by kintopp
One-shot object detection
This is more advanced than classification, which only tells you what the “main subject” of the image is — whereas object detection can find multiple objects, classify them, and locate where they are in the image. via Pocket
analysis  images  learn  recognition  classification  ml  deep 
june 2018 by kintopp
Paperspace: Cloud Machine Learning, AI, and effortless GPU infrastructure.
We are building out the world's most powerful GPU cloud. Accelerate your ML, AI, and data science workflow. Paperspace is trusted by some of the world's most respected companies. Learn more about how Paperspace can work for your business. via Pocket
cloud  gpu  infrastructure  ml  deep 
june 2018 by kintopp
GitHub - dhlab-epfl/dhSegment: Generic framework for historical document processing
dhSegment allows you to extract content (segment regions) from different type of documents. See some examples here. The corresponding paper is now available on arxiv. via Pocket
images  deep 
may 2018 by kintopp
Apache MXNet in the Wolfram Language - O'Reilly Media
Let’s consider a simple example of training a net that operates on variable-length sequences. We will train a net to take a simple arithmetic sum, represented by a string (e.g., 43+3), and predict the output value (e.g., 46). First, we must generate some training data: via Pocket
wolfram  deep 
may 2018 by kintopp
Neural Networks in the Wolfram Language—Wolfram Language Documentation
Wolfram Language tutorial/introduction to training and deployment of neural network machine learning systems.
deep  documentation  learn  wolfram 
may 2018 by kintopp
Artificial intelligence: Commission outlines a European approach to boost investment and set ethical guidelines
Today the European Commission is presenting a series of measures to put artificial intelligence (AI) at the service of Europeans and boost Europe's competitiveness in this field. via Pocket
europe  funding  h2020  infrastructure  ml  deep 
may 2018 by kintopp
Lobe - Deep Learning Made Simple
Teach your app to see emotions. Build, train, and ship custom deep learning models using a simple visual interface. Build, train, and ship custom deep learning models using a simple visual interface. Teach your app to see emotions. via Pocket
api  cloud  deep  dev  interactive  tools  webdev 
may 2018 by kintopp
This AI Paints Like The Old Masters. Can You Tell The Difference?
Discussions of AI and machine learning are often buttoned-up–and for good reason, considering their society-shaping impact. But art made by neural networks has become a field in its own right, full of people training algorithms to emulate people’s faces, fireworks, and even naked women. via Pocket
art  demos  deep 
may 2018 by kintopp
What is dhSegment? | dhSegment
It is a generic approach for Historical Document Processing. It relies on a Convolutional Neural Network to do the heavy lifting of predicting pixelwise characteristics. Then simple image processing operations are provided to extract the components of interest (boxes, polygons, lines, masks, …) via Pocket
analysis  images  manuscripts  ocr  deep 
april 2018 by kintopp
GitHub - SeguinBe/DHWorkshop2017: Code for the hands-on of the Computer Vision and Digital Humanities Workshop DHCONF2017
This repository contains the code used for the hands-on session of the "Computer Vision for Digital Humanities" Workshop on the 7th of August during the DHCONF2017. via Pocket
art  demos  images  learn  workshop  deep 
april 2018 by kintopp
Microsoft Azure Cognitive Services
Infuse your apps, websites and bots with intelligent algorithms to see, hear, speak, understand and interpret your user needs through natural methods of communication.
api  language  recognition  speech  dev  algorithm  ml  deep 
march 2018 by kintopp
What Neural Networks look - Online Technical Discussion Groups—Wolfram Community
This is the pelican which Neural Networks(VGG-16) look. I referred this post to the presentation by Markus van Almsick at the 2017 Wolfram Technology Conference. via Pocket
visualization  wolfram  deep 
march 2018 by kintopp
Neural networks and deep learning
The human visual system is one of the wonders of the world. Consider the following sequence of handwritten digits: Most people effortlessly recognize those digits as 504192. That ease is deceptive. via Pocket
images  learn  recognition  deep 
march 2018 by kintopp
The Building Blocks of Interpretability | Google Open Source Blog
Cross-posted on the Google Research Blog. In 2015, our early attempts to visualize how neural networks understand images led to psychedelic images. Soon after, we open sourced our code as DeepDream and it grew into a small art movement producing all sorts of amazing things. via Pocket
learn  visualization  deep 
march 2018 by kintopp
Nanne van Noord publications
Nanne van Noord. Postdoc, University of Amsterdam
Machine Learning Computer Vision Representation Learning
analysis  art  images  netherlands  ml  deep 
january 2018 by kintopp
Eric Postma | Artificial Intelligence Researcher | JADS & Tilburg University
Eric Postma (1961) is a professor in Artificial Intelligence at the Cognitive Science & AI department at Tilburg University and at the Jheronimus Academy of Data Science in ‘s-Hertogenbosch. He received his M.Sc. via Pocket
analysis  art  images  netherlands  research  ml  deep 
january 2018 by kintopp
LSTMS | Recurrent Neural Network LSTM | Deep Learning Network
The first time I learned about LSTMs, my eyes glazed over. Not in a good, jelly donut kind of way. via Pocket
learn  deep 
january 2018 by kintopp
Computer vision so good. – Barnes Foundation – Medium
Many of you are following the stories about the revamp of our collection online and the use of computer vision to create needed keywords, but also to identify objects that share visual similarities. via Pocket
art  paper  ml  deep 
december 2017 by kintopp
Insight – Intelligent Neural Systems as InteGrated Heritage Tools
The recently started BELSPO-funded INSIGHT project (Intelligent Neural Systems as Integrated Heritage Tools) organizes a launch event on 9 November 2017. via Pocket
analysis  art  arthistory  belgium  culture  deep  images 
december 2017 by kintopp
Home - colah's blog
Neural Networks (General) Recurrent Neural Networks Convolutional Neural Networks Visualizing Neural Networks Miscellaneous Traditional Papers via Pocket
learn  deep 
october 2017 by kintopp
Using computer vision to tag the collection. – Barnes Foundation – Medium
Throughout our collection project we’ve talked about the use of computer vision and machine learning to help us determine visual relationships. In addition, we’ve used these same tools to analyze images and provide subject keywords for searching. via Pocket
art  catalog  museums  deep 
october 2017 by kintopp
The 9 Deep Learning Papers You Need To Know About &#40&#85nderstanding C&#78&#78s Part 3&#41 – Adit Deshpande – CS Undergrad at UCLA ('19)
In this post, we’ll go into summarizing a lot of the new and important developments in the field of computer vision and convolutional neural networks. We’ll look at some of the most important papers that have been published over the last 5 years and discuss why they’re so important. via Pocket
learn  paper  deep 
october 2017 by kintopp
Wolfram Neural Net Repository
The Wolfram Neural Net Repository is a public resource that hosts an expanding collection of trained and untrained neural network models, suitable for immediate evaluation, training, visualization, transfer learning and more. via Pocket
resources  wolfram  deep 
october 2017 by kintopp
We're learning more about how neural nets work...
To see more from Science on Facebook, log in or create an account.To see more from Science on Facebook, log in or create an account. via Pocket
learn  video  deep 
october 2017 by kintopp
When not to use deep learning
So, when is deep learning not ideal for a task? From my perspective, these are the main scenarios where deep learning is more of a hinderance than a boon. Deep nets are very flexible models, with a multitude of architecture and node types, optimizers, and regularization strategies. via Pocket
deep  learn 
september 2017 by kintopp
DeepL Translator
I'm impressed by the DeepL Translator. It captures the nuances of different languages and manages to translate them. The system has a mastery of syntax beyond anything I've seen from other automatic translation services. via Pocket
tools  translation  ml  deep 
september 2017 by kintopp
302 Found
When I was writing books on networking and programming topics in the early 2000s, the web was a good, but an incomplete resource. Blogging had started to take off, but YouTube wasn’t around yet, nor was Quora, Twitter, or podcasts. via Pocket
learn  lists  resources  ml  deep 
august 2017 by kintopp
NLP and Python Machine Learning tutorials
While machine learning has a rich history dating back to 1959, the field is evolving at an unprecedented rate. In a recent article, I discussed why the broader artificial intelligence field is booming and likely will for some time to come. via Pocket
dev  learn  python  resources  ml  deep 
august 2017 by kintopp
Neural Networks in iOS 10 and macOS
Apple has been using machine learning in their products for a long time: Siri answers our questions and entertains us, iPhoto recognizes faces in our photos, Mail app detects spam messages. via Pocket
ios  osx  resources  deep 
august 2017 by kintopp
Computed Curation — Office of Philipp Schmitt
Computed Curation is a photobook created by a computer. Taking the human editor out of the loop, it uses machine learning and computer vision tools to curate a series of photos from an archive of pictures. via Pocket
art  deep  demos  images 
july 2017 by kintopp
The Digital Humanities Project: Aesthetics at the Intersection of Art and Science
Welcome to the “Digital Humanities Project: Aesthetics at the Intersection of Art and Science” web page by art historian Emily L. Spratt and computer scientist Ahmed Elgammal. via Pocket
analysis  art  arthistory  computing  deep  ml 
may 2017 by kintopp
Everything that’s wrong with FaceApp, the latest creepy photo app for your face - The Washington Post
FaceApp is an app for ruining your face. It morphs you into an old (or in my case, an even older) person. It can change your appearance to make you look more masculine or feminine, maybe. Take a photo with a neutral expression, and it will force you to smile. via Pocket
deep  ethics  privacy  ml 
april 2017 by kintopp
Call for Abstracts | AVinDH SIG
Although the majority of Digital Humanities scholars still focus on textual analysis, we see an increasing number of studies using digitised visual sources and taking the first steps in the field of ‘Visual Big Data’ (Ordelman et al, 2014). via Pocket
canada  cfp  conference  deep  images  ml 
april 2017 by kintopp
Creating a Modern OCR Pipeline Using Computer Vision and Deep Learning | Dropbox Tech Blog
In this post we will take you behind the scenes on how we built a state-of-the-art Optical Character Recognition (OCR) pipeline for our mobile document scanner. via Pocket
analysis  deep  images  ocr  report  ml 
april 2017 by kintopp
GitHub - RaRe-Technologies/gensim: Topic Modelling for Humans
Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community. via Pocket
deep  python  tools  topics  vectors  text 
april 2017 by kintopp
Deep Learning Applied to Computer Vision - YouTube
For the latest information, please visit: Mattias OdisioWolfram developers and colleagues discussed the latest in innovative technologies for cloud computing, interactive deployment, mobile devices, and more. via Pocket
deep  images  talk  video  wolfram 
april 2017 by kintopp
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Jun-Yan Zhu* Taesung Park* Phillip Isola Alexei A. Efros UC Berkeley [Paper] [GitHub] Abstract Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. via Pocket
art  images  paper  deep 
april 2017 by kintopp
Visual Geometry Group Home Page
Retrieve objects or scenes in a movie with the ease, speed and accuracy with which Google retrieves web pages containing particular words. Retrieve shots containing particular people/actors in video using an imaged face as the query. via Pocket
art  datasets  images  oxford  recognition  search  ml  deep 
april 2017 by kintopp
implementation details - how to take ImageFeatures? - Mathematica Stack Exchange
Feature extraction is a very important idea in machine learning. It allows us to build a neural network based on some of the pretrained high-performance networks. This is also the idea behind transfer learning. via Pocket
learn  mathematica  ml  deep 
april 2017 by kintopp
VQA: Visual Question Answering
Introducing VQA v2.0: A More Balanced and Bigger VQA Dataset! VQA is a new dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer. via Pocket
analysis  images  learn  text  ml  deep 
february 2017 by kintopp
GitHub - jfrancis71/CognitoNet: Feedforward Convolutional Neural Network for Mathematica
Welcome to CognitoNet. CognitoNet is a Mathematica implementation of Convolutional Neural Nets. Objectives of the project are to make neural networks: Accessible Interactive Flexible Extensible Efficient in descending order of priority. via Pocket
mathematica  deep 
january 2017 by kintopp
Deep Learning Lecture 1: Introduction - YouTube
Slides available at: taught in 2015 at the University of Oxford by Nando de Freitas with great help from Brendan Shillingford. via Pocket
learn  deep 
january 2017 by kintopp
Deep Learning
Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. This website is intended to host a variety of resources and pointers to information about Deep Learning. via Pocket
learn  resources  deep 
january 2017 by kintopp
ConvNetJS: Deep Learning in your browser
The library allows you to formulate and solve Neural Networks in Javascript, and was originally written by @karpathy (I am a PhD student at Stanford). However, the library has since been extended by contributions from the community and more are warmly welcome. Current support includes: via Pocket
demos  learn  deep 
january 2017 by kintopp
A Shallow Tour of Deep Learning - YouTube
Speaker: S. BodensteinGet the basics of neural networks and applications such as image/speech recognition, image captioning, question answering, and game playing. A case study of the ImageIdentify built-in Wolfram Language symbol and discussion of the future of deep learning.Download notebook: http: via Pocket
learn  deep 
january 2017 by kintopp
Deep Learning for Computer Vision with Python [ eBook ] by Adrian Rosebrock —Kickstarter
This book has one goal — to help developers, researchers, and students just like yourself become experts in deep learning for image recognition and classification. via Pocket
books  python  deep 
january 2017 by kintopp
Computer Vision Algorithms Detect Human Figures In Cubist Art
The human visual system has evolved to recognise people in almost any pose under a vast range of lighting conditions. via Pocket
arthistory  analysis  recognition  images  deep 
december 2016 by kintopp
GitHub - inejc/painters: Winning solution for the Painter by Numbers competition on Kaggle
This repository contains a 1st place solution for the Kaggle competition Painter by Numbers. Below is a brief description of the dataset and approaches I've used to build and validate a predictive model. via Pocket
arthistory  analysis  recognition  images  deep 
december 2016 by kintopp
Machine Learning is Fun! – Adam Geitgey – Medium
Update: Machine Learning is Fun! Part 2, Part 3, Part 4 and Part 5 are now available! You can also read this article in 日本語, Português, Türkçe, Français, 한국어 or العَرَبِيَّة‎‎, or Español (México). via Pocket
learn  ml  deep 
december 2016 by kintopp
Understanding Convolution in Deep Learning - Tim Dettmers
Convolution is probably the most important concept in deep learning right now. It was convolution and convolutional nets that catapulted deep learning to the forefront of almost any machine learning task there is. via Pocket
learn  ml  deep 
december 2016 by kintopp
Late one Friday night in early November, Jun Rekimoto, a distinguished professor of human-computer interaction at the University of Tokyo, was online preparing for a lecture when he began to notice some peculiar posts rolling in on social media. via Pocket
ml  deep 
december 2016 by kintopp
Nuts and Bolts of Applying Deep Learning (Andrew Ng) - YouTube
The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to each below in case that's useful for people who want to watch specific talks several times (like I do). Please check out the official website via Pocket
learn  lecture  talk  deep 
december 2016 by kintopp
Tombone's Computer Vision Blog: Nuts and Bolts of Building Deep Learning Applications: Ng @ NIPS2016
In addition to these four accuracies, you might want to report the human-level accuracy, for a total of 5 quantities to report. The difference between human-level and training set performance is the Bias. The difference between the training set and the training-dev set is the Variance. via Pocket
methodology  deep 
december 2016 by kintopp
Two Weeks of Colorizebot - Conclusions and Statistics
About two weeks ago we released ColorizeBot to wander around Reddit. This Reddit bot has started by coloring images on r/OldSchoolCool and a day after, has spread all over Reddit. The bot was based on a pre-trained neural network - More information on the netwrok can be found here. via Pocket
classification  colour  demos  images  deep 
september 2016 by kintopp
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