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Learning_Theories. Network Visualization guest lecture at #DataVizQMSS at @Columbia. Model Thinking. This course will consist of twenty sections.

Model Thinking

As the course proceeds, I will fill in the descriptions of the topics and put in readings. Section 1: Introduction: Why Model? In these lectures, I describe some of the reasons why a person would want to take a modeling course. These reasons fall into four broad categories: Articles & Posts. How sense and respond organizations differ from make and sell organizations. S&R as Post-Industrial Managerial Paradigm An adaptive management paradigm is the missing element in current attempts to transform businesses into adaptive organizations.

How sense and respond organizations differ from make and sell organizations.

Because adaptive behavior is typically unplanned—often ad hoc– it is intrinsically inefficient and therefore persistently undermined by the existing efficiency-centric management paradigm. The metrics and practices fostered by this industrial age model frustrate attempts to empower people, inculcate a customer orientation, leverage adaptive technologies, and respond to unanticipated change.

Chaire Machine Learning for Big Data. L’accélération du développement et des usages des technologies de l’information et de la communication nous a indéniablement projeté dans une nouvelle ère numérique, celle du « Big Data », où la ...

Chaire Machine Learning for Big Data

L’accélération du développement et des usages des technologies de l’information et de la communication nous a indéniablement projeté dans une nouvelle ère numérique, celle du « Big Data », où la collecte, le stockage et l’accès à des données toujours plus massives offrent la perspective de progrès considérables dans de nombreux secteurs d’activité, poursuivant dans la voie tracée par les grands acteurs de l’internet. Machine Learning/AI/Etc | Tech Topics. BigML - Machine Learning Made Easy. Stanford Machine Learning. Machine Learning & Big Data. Artificial neural network. An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain.

Artificial neural network

Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons.

This process is repeated until finally, an output neuron is activated. This determines which character was read. Brain Project Comparison. The BioRC Biomimetic Real-Time Cortex Project. Neural Network. Machine - Learning. Machine Learning & Artificial Neural Networks. In machine learning. Machine-learning. Machine Learning/AI/Etc | Tech. Machine-Learning & NNGA. Machine Learning / AI. Open Source: the Meritocracy vs the Circle of Trust. There has been this idea running around the back of my head for a while, and it's only now that it is starting to crystalize into something that I can express.

Open Source: the Meritocracy vs the Circle of Trust

When we look at Open Source projects, we see that there is a hierarchy of involvement. There are different levels at which you can be involved, and at each higher level, there will be less and less individuals. For now I am going to divide involvement up like this: Different Open Source projects but different barriers at different points. For example: (Note: I'm not going to even try and pretend that the above is a complete list of road-blocks) Different people view success of Open Source projects differently. But I don't want to go down such an academic road today. 10 open source e-learning projects to watch - Collaboration - Open Source. As corporate and government organizations embrace the Web for delivering more education and training programs, a wealth of free and open source e-learning applications will help lower the barrier to entry.

10 open source e-learning projects to watch - Collaboration - Open Source

TechWorld looks at the options. ATutor ATutor is a Web-based learning content management system (LCMS) designed for accessibility and adaptability by the Adaptive Technology Resource Centre at the University of Toronto. Humans TXT: We Are People, Not Machines. Réseaux pour le e-learning.

SMED. Living Lab. Map Tales. La technologie peut-elle éliminer la pauvreté ? (2/2) : Distinguer le potentiel des machines de celui des hommes. Apprentissage des langues et Réseaux Médias Sociaux - Language learning and social networks. Les réseaux sociaux peuvent jouer un rôle décisif dans le domaine de l’enseignement d'une langue étrangère, car ils favorisent la communication réelle, le travail collaboratif entre apprenants, entre classes et entre enseignants , ainsi que le développement des échanges et contacts interculturels.

Apprentissage des langues et Réseaux Médias Sociaux - Language learning and social networks

"Members of online communities learn by making and developing connections (intentionally or not) between ideas, experiences, and information, and by interacting,sharing, understanding, accepting, commenting, creating and defending their own opinions, their view points, their current situations and their daily experiences. Online communities allow, form, guide, foster, and stimulate connections. Learning in online communities takes place through storytelling, making jokes, giving examples, linking and making available different resources, asking questions, providing answers, developing empathy, and simply reading, to list a few examples. Curation. Organigramme du blended learning.

Elearning et mobilité. Hype Cycle for Emerging Technologies. Envisioning the future of education. Organigramme de la pensée d'un adulte. eLearning. 100 sites pour consommer sans posséder. Voici une première tentative (à ma connaissance) de création d’une liste d’initiatives françaises (ou ayant des activités en France) sur la consommation collaborative (voir la définition au bas de cet article).

100 sites pour consommer sans posséder

Dans cette liste, le parti pris a été d’indiquer les initiatives relevant des formes nouvelles d’échange entre particuliers (partage, troc, échange, location) ainsi que les nouveaux styles de vie collaboratifs (crowdfunding, coworking, colunching ...) mais aussi des initiatives se situant aux limites du sujet mais qui méritaient d’être mises en avant pour l’innovation sociale qu’elles représentent. N’hésitez pas à apporter vos contributions à cette liste collaborative ! Alimentation Achat groupé direct au producteur * * * * Extension numérique du corps. Innovation - Créativité. Khan Academy.

Coursera. Connexions - Sharing Knowledge and Building Communities. How To Find Inspiration In The Age Of Information Overload ⚙ Co. I recently came across a quote from spoken word poet Phil Kaye’s Repetition.

How To Find Inspiration In The Age Of Information Overload ⚙ Co

In it, he says: My mother taught me this trick, If you repeat something over and over again, it loses it’s meaning...Our existence, she said, is the same way. You watch the sunset too often, and it just becomes 6pm. You make the same mistake over and over, you’ll stop calling it a mistake. If you just wake up, wake up, wake up, wake up, wake up, wake up one day you’ll forget why. Repetition voids meaning. Crack Open a Book Amazon is kind of like a drug dealer. My favorite of late is Steal Like An Artist by Austin Kleon. Go Against the Grain Whatever everyone else is writing about, you probably shouldn’t be. Andrew Chen writes “you’ll have to differentiate on expertise and insight, rather than trying to tag along on whatever cool topic we’re talking about these days.”

Tell A Story. Myself as a Commonwealth: Notes on the Science of Self. We imagine our view of the world like a painting from the Realism movement – rife with detail and comprehensible – but the contents of our conscious mind are more like a work of Pop Art, full of abstractions and open to interpretation. The problem, as any MOMA goer will confess, is that it’s difficult to interpret a world full of ambiguity. You can spend a career studying Rothko or Pollock and reach a different conclusion from the modern art professor down the hall. Worse, as any art historian will admit, even the artist struggles to explain his preferences.

Le marketing de contenu inquiète les diplômés du secteur quant au futur de leur profession. Why your brand should get creative now with visual content marketing. According to Toprank CEO Lee Odden’s recent SES London session, content marketing at its very minimum needs to include: brand leadership, customer empathy, storytelling and creativity.

Why your brand should get creative now with visual content marketing

Here are three reasons why some of that creativity should be visual, regardless of your brand or industry. Dedicated Vine analytics tools. Create a Marketing Movement from the Inside Out. The Neuroscience of Your Brain on Fiction. Création de contenus. CMS. Datamining, webmining. Formation professionnelle et continue - Conseil - Cegos - Organisme de formation - e-learning. Fluid Concepts and Creative Analogies. Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought is a 1995 book by Douglas Hofstadter and other members of the Fluid Analogies Research Group exploring the mechanisms of intelligence through computer modeling. It contends that the notions of analogy and fluidity are fundamental to explain how the human mind solves problems and to create computer programs that show intelligent behavior. It analyzes several computer programs that members of the group have created over the years to solve problems that require intelligence.

Origin of the book[edit] Copycat (software) Copycat is a model of analogy making and human cognition based on the concept of the parallel terraced scan, developed in 1988 by Douglas Hofstadter, Melanie Mitchell, and others at the Center for Research on Concepts and Cognition, Indiana University Bloomington. The original Copycat was written in Common Lisp and is bitrotten (as it relies on now-outdated graphics libraries); however, a Java port exists. Copycat produces answers to such problems as "abc is to abd as ijk is to what? " (abc:abd :: ijk:?). Semantic Web Patterns: A Guide to Semantic Technologies.

In this article, we'll analyze the trends and technologies that power the Semantic Web. We'll identify patterns that are beginning to emerge, classify the different trends, and peak into what the future holds. In a recent interview Tim Berners-Lee pointed out that the infrastructure to power the Semantic Web is already here. ReadWriteWeb's founder, Richard MacManus, even picked it to be the number one trend in 2008. And rightly so. Not only are the bits of infrastructure now in place, but we are also seeing startups and larger corporations working hard to deliver end user value on top of this sophisticated set of technologies. The Semantic Web means many things to different people, because there are a lot of pieces to it. The disagreement is not accidental, because the technology and concepts are broad. 1.

Top-Down: A New Approach to the Semantic Web. Earlier this week we wrote about the classic approach to the semantic web and the difficulties with that approach. While the original vision of the layer on top of the current web, which annotates information in a way that is "understandable" by computers, is compelling; there are technical, scientific and business issues that have been difficult to address.

One of the technical difficulties that we outlined was the bottom-up nature of the classic semantic web approach. Specifically, each web site needs to annotate information in RDF, OWL, etc. in order for computers to be able to "understand" it. Semantic Web. W3C's Semantic Web logo The Semantic Web is a collaborative movement led by international standards body the World Wide Web Consortium (W3C).[1] The standard promotes common data formats on the World Wide Web. By encouraging the inclusion of semantic content in web pages, the Semantic Web aims at converting the current web, dominated by unstructured and semi-structured documents into a "web of data". The Semantic Web stack builds on the W3C's Resource Description Framework (RDF).[2] According to the W3C, "The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries.

While its critics have questioned its feasibility, proponents argue that applications in industry, biology and human sciences research have already proven the validity of the original concept. Linked Data. A Periodic Table of Visualization Methods. IdeaCast. [tel-00778089, v1] Biométrie par signaux physiologiques. Visionneuse Google Drive. Intelligence artificielle : une machine qui apprend le sens des mots par elle-même. Machine Learning Group (MLG) - Laboratoire d'Apprentissage Automatique de l'ULB.

Machine Learning. Classification. Sentiment Classification using Machine Learning Techniques. Courses. Million Lines of Code. 31677_a_large. Deep learning. Connected Learning Infographic. Education, post-structuralism and the rise of the machines. Rhizomatic Learning - The community is the curriculum. PredictionIO Open Source Machine Learning Server. 20 Resources for Teaching Kids How to Program & Code. Visualization frameworks and tools. Pourquoi le machine learning cartonne dans la Silicon Valley. BMII: Brain Machine Interfacing Initiative.

Towards Reproducible Descriptions of Neuronal Network Models. Machine Learning Cheat Sheet (for scikit-learn) WP 3 NoSQL Big Data. DATA MINING. L'essentiel de l'actualité du serious game sur un blog ! Motivational Theories and Design.

This page was originally authored by Diana Bang (2011). Opensource. Les différentes carrières du consultant décisionnel. Creative Commons France. Freemages, banque de photos gratuites et Libres à télécharger. Outils et techniques de recherche d'informations. STATS. Comprendre le business SaaS. 10 conseils pour passer au SaaS progressivement. Azure Machine Learning : déplacer l'apprentissage automatique dans le cloud. Cloud : 5 conseils pour mettre d’accord la DSI et les directions métiers Jérôme Martin, BearingPoint.

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