data science

The O'Reilly Data Show explores the opportunities and techniques driving big data and data science. Through interviews and analysis, we highlight the people putting data to work.

The O'Reilly Data Show Podcast

Oye con atención,y así aprendemos,mas.
Un aviso o consejo te puede salvar tu vida o la de un familiar o amigo cercano
Tu debes saber acerca de primeros auxilios.Ayuda a salvar vidas.!
Sabes como manejar un infarto del miocardio ? sabes cuales son las señales previas ? Que hacer en caso de una fractura,desmayo,intoxicación,falta de aire,en caso de choque automovilístico ,sabe como debe manejar a los afectados,o accidentados ?Conoce cuales son los números telefónicos en caso de emergencias:Cruz Roja,Defensa Civil,Bomberos,Policía,Sijin,Ejercito,Policía de Carreteras,CAI,Polfa,Essa,Acueducto,CDMB,Cía de teléfonos,Gasan,Whatsapp, emails,(correos electrónicos),Nos de celulares o móviles,teléfonos fijos,directos,lineas de emergencia,Lineas de atención de emergencias,123,#767,Hospitales y Clínicas, IPS,mas cercanos a su residencia,Banco de sangre,Estaciones de radio,Banda Ciudadana. Lleva siempre su carnet,o afiliaciones a su EPS,ARL ?El numero de la Cía de Seguros de su vehículo,que lo esta amparando?Nos de ambulancias,empresa de taxis,el numero de Tel. fijo o Cel. de su medico personal o familiar de cabecera,los Nos de Cel. de un familiar o amigo suyo? sabe cual es el tipo de sangre suya ? sabes a que es alérgico ? lleva consigo tus medicamentos ?

Primeros Auxilios-- Conocimiento - Aprendizaje-Historia

Technology

time series

El misterio de cómo pudieron los egipcios construir sin tecnología sus enormes pirámides siempre ha interesado a la humanidad. Los científicos ofrecen una nueva posible explicación.



Asi podrian haberse construido las piramides de egipto

¡Sábados 10:30 am! Radio Nicolaita: www.radionicolaita.umich.mx 1370 am/ 104.3 fm

De sudáfrica para el mundo: Nelson Mandela, una historia de lucha y rebeldía

¿Conoces la historia de la huida a Egipto? La biblia cuenta como un ángel le dijo a José que debía huir con su familia, ya que Herodes buscaba al mesías recién nacido para matarlo.
La escena de esta pintura corresponde al retorno de Egipto, es decir, al momento en que José, Jesús y María regresaron a su hogar. Su andar apacible nos evidencia toda ausencia de peligro, por lo que irradia una particular sensación de calma.
La composición horizontal otorga especial importancia al paisaje que rodea a la Sagrada Familia. La inclusión del paisaje en el arte colonial peruano fue un aspecto novedoso para la época. Esta es una característica propia de la pintura andina del siglo XVII y tiene como iniciador a Diego Quispe Tito.
El Retorno de Egipto se basó en una estampa del artista flamenco Pedro Pablo Rubens. ¿Identificas las diferencias entre la estampa y la pintura?


El Retorno de Egipto

Novela histórica del escritor finlandés Mika Waltari publicada en 1945. Narrada por Yamil Cuéllar en su programa Lecturas de Yamil Cuéllar. (Facebook: Lecturas de Yamil Cuéllar /Web: www.yamilcuellar.com)
https://www.facebook.com/lecturasdeyamil/

45-Sinuhé el Egipcio: La paz de Egipto

Hoy con: Irma Robledo Strauss y Nicolás Bejarano.
Tema: Los gigantes de Egipto.

RADIO ENERO 21-2019. TODELAR - Tema: Los gigantes de Egipto

In this episode of the Data Show, I spoke with Roger Chen, co-founder and CEO of Computable Labs, a startup focused on building tools for the creation of data networks and data exchanges. Chen has also served as co-chair of O'Reilly's Artificial Intelligence Conference since its inception in 2016. This conversation took place the day after Chen and his collaborators released an interesting new white paper, "Fair value and decentralized governance of data." Current-generation AI and machine learning technologies rely on large amounts of data, and to the extent they can use their large user bases to create “data silos,” large companies in large countries (like the U.S. and China) enjoy a competitive advantage. With that said, we are awash in articles about the dangers posed by these data silos. Privacy and security, disinformation, bias, and a lack of transparency and control are just some of the issues that have plagued the perceived owners of “data monopolies.”

Acquiring and Sharing High-Quality Data

In this episode of the Data Show, I speak with Cassie Kozyrkov, technical director and chief decision scientist at Google Cloud. She describes "decision intelligence" as an interdisciplinary field concerned with all aspects of decision-making, and which combines data science with the behavioral sciences. Most recently she has been focused on developing best practices that can help practitioners make safe, effective use of AI and data. Kozyrkov uses her platform to help data scientists develop skills that will enable them to connect data and AI with their organizations' core businesses.

We had a great conversation spanning many topics, including:

How data science can be more useful

The importance of the human side of data

The leadership talent shortage in data science

Is data science a bubble?

Make Data Science More Useful

In this episode of the Data Show, I speak with Alex Ratner, project lead for Stanford’s Snorkel open source project; Ratner also recently garnered a faculty position at the University of Washington and is currently working on a company supporting and extending the Snorkel project. Snorkel is a framework for building and managing training data. Based on our survey from earlier this year, labeled data remains a key bottleneck for organizations building machine learning applications and services.

Ratner was a guest on the podcast a little over two years ago when Snorkel was a relatively new project. Since then, Snorkel has added more features, expanded into computer vision use cases, and now boasts many users, including Google, Intel, IBM, and other organizations. Along with his thesis advisor professor Chris Ré of Stanford, Ratner and his collaborators have long championed the importance of building tools aimed squarely at helping teams build and manage training data. With today’s release of Snorkel version 0.9, we are a step closer to having a framework that enables the programmatic creation of training data sets.

Labeling, Transforming, and Structuring Training Data Sets for Machine Learning

In this episode of the Data Show, I speak with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learning and started teaching herself the ML tools on Amazon Web Services. Fast forward to today, Williams has built some well-regarded Alexa skills, mastered ML services on AWS, and has now firmly added machine learning to her developer toolkit.

Becoming a Machine Learning Practitioner

In this episode of the Data Show, I speak with Michael Mahoney, a member of RISELab, the International Computer Science Institute, and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of many important problems in large-scale data analysis. On the theoretical side, his works spans algorithmic and statistical methods for matrices, graphs, regression, optimization, and related problems. On the applications side, he has contributed to systems used for internet and social media analysis, social network analysis, as well as for a host of applications in the physical and life sciences. Most recently, he has been working on deep neural networks, specifically developing theoretical methods and practical diagnostic tools that should be helpful to practitioners who use deep learning.

Understanding Deep Neural Networks

In this episode of the Data Show, I speak with Arun Kejariwal of Facebook and Ira Cohen of Anodot (full disclosure: I’m an advisor to Anodot). This conversation stemmed from a recent online panel discussion we did, where we discussed time series data, and, specifically, anomaly detection and forecasting. Both Kejariwal (at Machine Zone, Twitter, and Facebook) and Cohen (at HP and Anodot) have extensive experience building analytic and machine learning solutions at large scale, and both have worked extensively with time-series data. The growing interest in AI and machine learning has not been confined to computer vision, speech technologies, or text. In the enterprise, there is strong interest in using similar automation tools for temporal data and time series.

Machine Learning and Analytics for Time Series Data

O'Reilly Media spreads the knowledge of innovators. At O’Reilly, a big part of our business is paying attention to what’s new and interesting in the world of technology. We have a pretty good record at having anticipated some of the big technology developments in recent history. For instance, we launched the first commercial Web site, GNN, in 1993; we organized the meeting at which the term “open source” was first adopted; we were early investors in Blogger, which helped launch the blogging revolution; and more recently, our Web 2.0 conference launched a world-wide meme. We call this predictive sense the “O’Reilly Radar.” And while we’re certainly not always right, we are, at least, good at making interesting guesses.

Our methodology is simple: we draw from the wisdom of the alpha geeks in our midst, paying attention to what’s interesting to them, amplifying these weak signals, and seeing where they fit into the innovation ecology. Add to that the original research conducted by our Research team, and you start to get a good picture of what the technology world is thinking about.

Machine Learning and Analytics for Time Series Data

O'Reilly Radar