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TERENCE | Sito docente - Vittorini
TERENCE is a 3-year long project designing the first adaptive learning system for poor comprehenders, hearing and deaf, and for their educators. The learning material of TERENCE is made of books of stories and smart games for reasoning about stories, divided into difficult levels. via Pocket
children  games  learn  nlp  text 
8 days ago by kintopp
How European sailors learned celestial navigation | Aeon Essays
In 1673, in a North Sea skirmish that killed nearly 150 men, the French privateer Jean-François Doublet took a bullet that tossed him from the forecastle and broke his arm in two places. via Pocket
history  learn  mathematics  navigation  ships  teach 
25 days ago by kintopp
When not to use deep learning
I know it’s a weird way to start a blog with a negative, but there was a wave of discussion in the last few days that I think serves as a good hook for some topics on which I’ve been thinking recently. via Pocket
learn  neural 
28 days ago by kintopp
A Word is Worth a Thousand Vectors | Stitch Fix Technology – Multithreaded
Standard natural language processing (NLP) is a messy and difficult affair. It requires teaching a computer about English-specific word ambiguities as well as the hierarchical, sparse nature of words in sentences. At Stitch Fix, word vectors help computers learn from the raw text in customer notes. via Pocket
analysis  learn  ml  nlp  text 
5 weeks 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
Learning Wikibase | Learn how to install and manage Wikibase.
Wikibase is an open source software suite for running a collaborative knowledge base. One installation of it is Wikidata. Learn how to launch a Wikibase instance and work with it. Learn how to install Wikibase using Git and Composer, using the Wikibase Docker image or Open Stack. via Pocket
documentation  infrastructure  learn  linkeddata  rdf  wiki 
7 weeks ago by kintopp
Natural Language Processing is Fun! – Adam Geitgey – Medium
This article is part of an on-going series on NLP: Part 1, Part 2, Part 3. You can also read a reader-translated version of this article in 普通话. via Pocket
analysis  books  learn  nlp  text 
8 weeks ago by kintopp
Machine Learning Crash Course  |  Google Developers
Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises. via Pocket
google  learn  lecture  ml 
11 weeks ago by kintopp
🌻 The Best and Most Current of Modern Natural Language Processing
Over the last two years, the Natural Language Processing community has witnessed an acceleration in progress on a wide range of different tasks and applications. via Pocket
bestof  books  learn  lists  nlp  paper  resources 
12 weeks ago by kintopp
iSHCamp – International Spatial Humanities Sprint Camp | Dublin, Ireland | 24-26 Oct 2019
The first International Spatial Humanities Sprint Camp (iSHCamp) will be held in Dublin, Ireland, in Autumn 2019.  This camp is aimed at Early Career Researchers who have an interest in Spatial Humanities, and want to learn techniques and methodologies to further their scholarship. via Pocket
geo  learn  uk  workshop 
may 2019 by kintopp
The MLIF Book — Machine Learning is Fun!
Machine Learning is Fun! The Book This book is for anyone who is curious about machine learning and artificial intelligence. via Pocket
books  dev  howto  learn  python  statistics  ml 
may 2019 by kintopp
Introduction — The Straight Dope 0.1 documentation
Before we could begin writing, the authors of this book, like much of the work force, had to become caffeinated. We hopped in the car and started driving. Having an Android, Alex called out “Okay Google”, awakening the phone’s voice recognition system. via Pocket
dev  learn  documentation  ml 
may 2019 by kintopp
Introduction to Digital Humanities
Develop skills in digital research and visualization techniques across subjects and fields within the humanities.What you'll learn
What the term “digital humanities” means in different disciplines.
How common digital tools work and examples of projects using them.
How various file types can be used to create, gather, and organize data.
How to use command-line functions to analyze text.
How to use free tools to create visual text analysis.
dh  harvard  learn  usa 
may 2019 by kintopp
The Carpentries
We teach foundational coding and data science skills to researchers worldwide. via Pocket
dev  learn 
may 2019 by kintopp
Summer school 2017 | Bibliotheca Digitalis | Bibliothèques Humanistes
With the support of  Humanities at Scale (DARIAH-EU) and the City of Le Mans, and in partnership with Biblissima and the Centre d’Études Supérieures de la Renaissance of Tours. via Pocket
dariah  dh  france  history  learn  text  workshop 
may 2019 by kintopp
Netzwerkanalyse mit Gephi
Welcher Primärtext liegt der Analyse zugrunde? Erstellen Sie ein Figuren-Netzwerk und analysieren Sie digital Lessings Drama Emilia Galotti.
Welche Arbeitsschritte sollten vor der Analyse ausgeführt werden? Vor Beginn der Analyse muss Gephi heruntergeladen bzw. installiert und Netzwerkdaten zu Lessings Drama auf Ihren Rechner heruntergeladen werden.
Welche Funktionen bietet Ihnen Gephi? Mit Gephi können Sie tabellarische Übersichten erstellen, die in Netzwerkvisualisierungen umgewandelt werden. Die Netzwerkvisualisierungen sind auf vielfältige Weise individualisierbar.
Lösungen zu den Beispielaufgaben
Haben Sie die Beispielaufgaben richtig gelöst? Hier finden Sie Antworten.
germany  graphs  howto  learn  networks 
may 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
The FOSTER portal is an e-learning platform that brings together the best training resources addressed to those who need to know more about Open Science, or need to develop strategies and skills for implementing Open Science practices in their daily workflows.
learn  resources  research 
march 2019 by kintopp
BERT (Bidirectional Encoder Representations from Transformers) is a recent paper published by researchers at Google AI Language. It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks, including Question Answering (SQuAD v1. via Pocket
deeplearning  learn 
march 2019 by kintopp
Beginning Historical Network Analysis - Google Docs
What I wish I had known when I started using Network Analysis methods
graphs  learn  networks  resources 
february 2019 by kintopp
Let’s Tango: Computational Musicology Using Wikidata, MusicBrainz and the Wolfram Language—Wolfram Blog
This post discusses new Wolfram Language features from the upcoming release of Version 12. Copyable input expressions and a downloadable notebook version of this post will be available when Version 12 is released. via Pocket
sparql  wolfram  learn  metadata  music  api  wiki 
february 2019 by kintopp
OpenStreetMap is a free street level map of the world, created by an ever growing community of mappers. Anyone can edit OpenStreetMap. via Pocket
crowdsourcing  geo  learn  maps  tools 
february 2019 by kintopp
Supervised Classification: The Naive Bayesian Returns to the Old Bailey | Programming Historian
As of August 2016, the Old Bailey Online experienced some issues that are currently being resolved by their project team. One of those issues includes the temporary suspension of the API which are used as the basis of this tutorial. via Pocket
history  learn  ml  text  uk 
november 2018 by kintopp
The Digital Orientalist | Practical examples and theoretical reflections on the do's and don'ts of using digital tools for your study and research in Arabic and Islamic Studies.
Many people in universities have a Mac. Yet, the world still for the most part runs on Windows. This can be an issue when you wish to collaborate or make use of certain applications. Here are some pointers to think Many people in universities have a Mac. via Pocket
books  diy  handwriting  learn  manuscripts 
november 2018 by kintopp
Getting Started With Machine Learning — Smashing Magazine
What are the fundamentals of machine learning, and what are the necessary tools to evaluate risk and other concerns in a machine learning application? This article covers everything you need to get started. via Pocket
learn  ml 
november 2018 by kintopp
[1611.05118] The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives
Authors:Mohit Iyyer, Varun Manjunatha, Anupam Guha, Yogarshi Vyas, Jordan Boyd-Graber, Hal Daumé III, Larry Davis v1), last revised 7 May 2017 (this version, v2)) Abstract: Visual narrative is often a combination of explicit information and judicious omissions, relying on the vie via Pocket
comics  datasets  images  learn  ml  narrative  paper  text 
november 2018 by kintopp
Temporal Network Analysis with R | Programming Historian
If you are reading this tutorial, you might already have some experience modeling humanities data as a network. via Pocket
graphs  humanities  learn  networks  time 
november 2018 by kintopp
Reconciliation · OpenRefine/OpenRefine Wiki · GitHub
Reconciliation is a semi-automated process of matching text names to database IDs (keys). This is semi-automated because in some cases, machine alone is not sufficient and human judgement is essential. For example, given "Ocean's Eleven" as the name of a film, should it be matched to via Pocket
learn  reconcile  tools 
november 2018 by kintopp
SPARQL — the Query Language – FactGrid
Wikibase installations are – at this moment – best explored with the SPARQL query language. Specialists are able to write queries in SPARQL but this is not what you would do as a beginner. Most people take a look at an example of a query and then modify the example to suit heir needs. Here just briefly for the beginning a couple of useful links.
learn  sparql  resources 
november 2018 by kintopp
GitHub - jiemakel/dhintro: Material for the Introduction to Methods for Digital Humanities course at the University of Helsinki
This repository is an attempt to give someone from a humanities background the absolute basic tools needed to start delving into programming by reading and dissecting ready-made examples that abound on the Internet. via Pocket
finland  humanities  learn  python  teach 
november 2018 by kintopp
TensorFlow For Poets
TensorFlow is an open source library for numerical computation, specializing in machine learning applications. In this codelab, you will learn how to run TensorFlow on a single machine, and will train a simple classifier to classify images of flowers. via Pocket
classification  humanities  images  learn  python  ml 
november 2018 by kintopp
Python Crash Course | No Starch Press
Page 207: In "Try It Yourself" 10-6, TypeError should be ValueError. via Pocket
books  learn  python 
november 2018 by kintopp
This free archive is hosted as a public service of the Wolfram Foundation, providing open access to articles, books, essays, posts, educational materials and student projects created by and for the Wolfram Notebook user community. First introduced in 1988 with the release of Mathematica 1. via Pocket
demos  interactive  learn  repository  showcase  teach  wolfram 
november 2018 by kintopp
Topic Model Tutorial
The Usage is simple: You create a corpus.txt file in which each line corresponds to a document. Then you execute the promoss.jar with Store the document metadata separated by semicolons in a file named meta.txt. The documents have to be put in a file named corpus. via Pocket
howto  learn  text  topics  analysis 
october 2018 by kintopp
scikit-learn: machine learning in Python — scikit-learn 0.19.2 documentation
Identifying to which category an object belongs to. Predicting a continuous-valued attribute associated with an object. via Pocket
dev  learn  python  resources  ml 
september 2018 by kintopp
Representation Learning on Networks
Researchers in network science have traditionally relied on user-defined heuristics to extract features from complex networks (e.g., degree statistics or kernel functions). via Pocket
graphs  learn  networks  ml 
august 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
Online STEAM Courses From Top Universities | Kadenze
Kadenze brings together educators, artists, and engineers from leading universities across the globe to provide world-class education in the fields of art and creative technology. You need to enter some additional information before you can enter. via Pocket
learn  resources 
june 2018 by kintopp
Runway | Machine Learning for Everyone
Open-source state of the art machine learning models you can use everywhere.
art  demos  learn  osx  tools  ml 
june 2018 by kintopp
Some ways Wikidata can improve search and discovery | Bodleian Digital Library
I have written in the past about how Wikidata enables entity-based browsing, but search is still necessary and it is worth considering how a semantic web database can be useful to a search engine index. via Pocket
graphs  learn  reconcile  search  semantic  wiki 
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
Distill About Prize Submit Feature Visualization How neural networks build up their understanding of images Feature visualization allows us to see how GoogLeNet, trained on the ImageNet dataset, builds up its understanding of images over many layers. via Pocket
cognition  learn  ml  visualization 
may 2018 by kintopp
Usecase: Reconciling von Geodaten - histHub Blog OpenRefine
HistHub befasst sich in einer Blogserie mit der Aufbereitung und Anreicherung von Daten in Openrefine. Der heutige Beitrag widmet sich einem konkreten Anwendungsfall. Im Folgenden werden wir beschreiben, wie wir für die Vernetzung der Geodaten verschiedener Provider vorgegangen sind. via Pocket
geo  learn  reconcile 
may 2018 by kintopp
Copyright Law Basics For UK Software Developers — Smashing Magazine
You can learn more about copyright law in general and about how it applies to software in my previous article. Go to article → “You must unlearn what you have learned!” Meet the brand new episode of SmashingConf San Francisco with smart front-end tricks and UX techniques. via Pocket
copyright  ip  learn  uk 
april 2018 by kintopp
Machine Learning: The Wolfram Approach
Top performance Wolfram Machine Learning uses the latest methods and libraries, with full support for GPUs and emerging hardware and software standards Full spectrum of methods State-of-the-art support for classic machine-learning methods (logistic regression, SVM, random forests, ... via Pocket
learn  wolfram  ml 
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
Exploring Mathematics with Mathematica: Dialogs Concerning Computers and ... - Theodore W. Gray, Jerry Glynn - Google Books
Exploring Mathematics with Mathematica: Dialogs Concerning Computers and Mathematics Theodore W. Gray, Jerry Glynn Addison-Wesley, 1991 - Mathematics - 535 pages 0 Reviews via Pocket
books  learn  wolfram 
april 2018 by kintopp
evaluation - Mathematica for Computer Scientists - Mathematica Stack Exchange
OK, I will start with a few suggestions. I think, that what you really need is to understand Mathematica evaluator. Once you get this understanding, programming in Mathematica will become vastly easier for you, and you will be ready for the advanced examples showing the power of rules. via Pocket
books  learn  wolfram 
april 2018 by kintopp
string manipulation - Programming paradigm change - Mathematica Stack Exchange
Stack Exchange network consists of 173 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. via Pocket
learn  wolfram 
april 2018 by kintopp
Where can I find examples of good Mathematica programming practice? - Mathematica Stack Exchange
I consider myself a pretty good Mathematica programmer, but I'm always looking out for ways to either improve my way of doing things in Mathematica, or to see if there's something nifty that I haven't encountered yet. Where (books, websites, etc. via Pocket
howto  learn  resources  wolfram 
april 2018 by kintopp
How to think in graphs: An illustrative introduction to Graph Theory and its applications
Graph theory represents one of the most important and interesting areas in computer science, and at the same time the most misunderstood (at least by me, no stats). Understanding and using graphs makes us better programmers. Thinking in graphs makes us the best. via Pocket
graphs  learn  networks 
march 2018 by kintopp
A Theory of Vibe — Glass Bead
Across the foliated space of the twenty-seven equivalents, Faustroll conjured up into the third dimension: From Baudelaire, E. A. Poe’s Silence, taking care to retranslate Baudelaire’s translation into Greek. via Pocket
learn  ml  neural  text 
march 2018 by kintopp
GitHub - Nanne/python-course: Tutorial and introduction into programming with Python for the humanities
We are going to use the method from the caffe guys, I think it's not that overdone: The only difference we are going to make is that we are only going to have a master branch and no dev. First get your own copy of the repo. via Pocket
learn  python 
march 2018 by kintopp
Distill — Latest articles about machine learning
By creating user interfaces which let us work with the representations inside machine learning models, we can give people new tools for reasoning. How neural networks build up their understanding of images. via Pocket
learn  resources  ml 
march 2018 by kintopp
Complex Network Analysis in Python: Recognize → Construct → Visualize → Analyze → Interpret by Dmitry Zinoviev | The Pragmatic Bookshelf
Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. via Pocket
graphs  learn  networks  python 
march 2018 by kintopp
Machine Learning Glossary  |  Google Developers
This glossary defines general machine learning terms as well as terms specific to TensorFlow. A statistical way of comparing two (or more) techniques, typically an incumbent against a new rival. via Pocket
learn  ml 
march 2018 by kintopp
Frictionless Data
Lightweight standards and tooling to make it effortless to get, share, and validate data. Data Packages are a lightweight containerization format for data. They provide the foundation for frictionless data transport. via Pocket
datasets  infrastructure  learn  resources  standards  tools 
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 7 best deep learning books you should be reading right now - PyImageSearch
In today’s post I’m going to share with you the 7 best deep learning books (in no particular order) I have come across and would personally recommend you read. via Pocket
books  learn  ml  neural 
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
Data Science at the Command Line
Please note that this website is currently slightly out-dated with respect to the published book.
analysis  data  learn  python  resources 
january 2018 by kintopp
Tutorials and Resources – Archaeological Networks
This tutorial is a step-by-step guide to network creation, visualisation and analysis using the free to use software Visone, through an archaeological case study on Maya obsidian networks in Mesoamerica. Weidele, D., and Brughmans, T. (2015) Network Analysis with Visone Tutorial. via Pocket
archaeology  learn  networks  tools 
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
Towards Data Science
Towards Data Science. Sharing concepts, ideas, and codes
algorithm  learn  resources  statistics  ml 
january 2018 by kintopp
Geocoding Historical Data using QGIS | Programming Historian
Many types of sources used by historians are inherently spatial. For example: In this tutorial, you will learn how to ‘geocode’ historial data containing placenames (towns, counties, countries, etc), thus making them mappable using QGIS, a digital mapping software suite. via Pocket
geo  learn  tools 
january 2018 by kintopp
Pelagios Gazetteer Interconnection Format · pelagios/pelagios-cookbook Wiki · GitHub
In order to enable interoperation between gazetteers, Pelagios defines a common “baseline model” – a conceptual and syntactical least common denominator that all our gazetteers map to. via Pocket
geo  learn  linkeddata  metadata 
january 2018 by kintopp
Data School
Page 1 of 5 | Older → via Pocket
data  learn  python  resources  ml 
december 2017 by kintopp
Imj: A web-based tool for visual culture macroanalytics – Zach Whalen
So-called “movie barcodes” are both elegant to look at and useful ways to explore how color schemes and designs shift throughout a film. via Pocket
analysis  film  learn  tools  video 
december 2017 by kintopp
Exploring and Analyzing Network Data with Python | Programming Historian
n.b.: This is a tutorial for exploring network statistics and metrics. We will therefore focus on ways to analyze, and draw conclusions from, networks without visualizing them. via Pocket
learn  networks  python 
december 2017 by kintopp
Analyzing data networks – Graph Commons – Medium
Analyzing data with visual methods helps you gain better insight about complexity. via Pocket
graphs  learn  networks 
december 2017 by kintopp
Wikimedia Research/Showcase - MediaWiki
The Monthly Wikimedia Research Showcase is a public showcase of recent research by the Wikimedia Foundation's Research Team, other WMF researchers and occasionally guest presenters. The showcase is hosted at the Wikimedia Foundation every 3rd Wednesday of the month at 11. via Pocket
data  learn  research  tools  wiki 
december 2017 by kintopp
Geoparsing English-Language Text with the Edinburgh Geoparser | Programming Historian
This is a lesson on how to use the Edinburgh Geoparser. The Geoparser allows you to process a piece of text and extract and resolve the locations contained within it. Among other uses, geo-resolution of locations makes it possible to map the data. via Pocket
analysis  geo  learn  tools  text 
november 2017 by kintopp
Machine Learning Resources | Machine Learning, Deep Learning, and Computer Vision
These are the resources you can use to become a machine learning or deep learning engineer. All of the resources are available for free online. Please check their respective licenses. via Pocket
learn  resources  ml 
november 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
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
Excel vs R: A Brief Introduction to R
Quantitative research often begins with the humble process of counting. Historical documents are never as plentiful as a historian would wish, but counting words, material objects, court cases, etc. can lead to a better understanding of the sources and the subject under study. via Pocket
excel  learn  statistics 
october 2017 by kintopp
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