Podcasts about colaboratory

Attendees at IMEX America are in for a special treat in the form of the Google Xi CoLaboratory, where the tech giant is teaming up with other exhibitors and speakers to stage sessions and spaces focusing on experience design and inclusion. Meetings Today's Tyler Davidson spoke with Kate Walsh, Megan Henshall and Naomi Clare Crellin to discover what Hello! Destination Management, Google and Storycraft Lab are cooking up in concert with other collaborators such as Marriott and Maritz on the exhibit floor at IMEX America 2023, and how the message they are delivering resonates beyond the annual event.

Andrew Heddle is the Head of New Business Development at Colaboratory, a SaaS company that uses data, science, and software to boost brand collaborations. As an entrepreneurial business leader, he has a track record of scaling e-commerce, D2C, and retail businesses. Andrew is also the author of D2C DNA, which examines in-depth case studies of the world's most innovative D2C brands. Before Colaboratory, Andrew was the Managing Director of D2C Commerce at VMLY&R and has held executive positions at top brands like Best Buy and Vista Outdoor Inc. In this episode… While companies might reach primary targets independently, combining marketing resources and creating joint campaigns with other non-competing brands could help reach new audiences. Brand collaborations create new and exciting reasons for consumers to shop, enabling businesses to develop credibility and authority. However, brands encounter challenges acquiring suitable partners and establishing and launching campaigns. Business strategy guru Andrew Heddle emphasizes the value of leveraging data and software to identify potential brand partnerships. He describes a successful brand collaboration as two businesses leveraging each other's strengths to offer value. But to have a successful campaign, a brand must remain authentic and create a strategy that connects with the audience organically. Is your partnership giving consumers value? In this episode of the Up Arrow Podcast, William Harris welcomes Andrew Heddle, the Head of New Business Development at Colaboratory, to discuss the elements of effective brand collaboration. Andrew shares tips for creating a campaign that cuts through the noise, the importance of synergy in a partnership, and the potential pitfalls of brand collaborations. He also highlights the inspiration behind Colaboratory, the firm's services, and case studies of successful partnerships. Tune in!

Colaboratory, también llamado Colab, es un producto de Google Research. Colab permite que todos puedan escribir y ejecutar código arbitrario de Python en el navegador. Es ideal para aplicarlo en proyectos de aprendizaje automático, análisis de datos y educación. En términos técnicos, Colab es un servicio de notebooks de Jupyter alojados que no requiere instalación para usarlo y brinda acceso sin costo a recursos computacionales, incluidas GPU. ¿Realmente se puede usar sin costo? Sí. Colab es un producto sin costo. Parece demasiado bueno para ser cierto. ¿Qué limitaciones tiene? Los recursos de Colab no están garantizados ni son ilimitados y, en ocasiones, los límites de uso fluctúan. Esto es necesario para que Colab pueda brindar recursos sin costo. Aquellos usuarios que busquen un acceso más confiable a mejores recursos pueden probar Colab Pro. Los recursos en Colab tienen prioridad para casos de uso interactivos. Prohibimos las acciones asociadas con el procesamiento masivo, las acciones que tengan un impacto negativo en otros, así como las acciones asociadas con el incumplimiento de nuestras políticas. No se permiten las siguientes acciones en los entornos de ejecución de Colab: hosting de archivos, entrega de contenido multimedia o cualquier oferta de servicios web no relacionada con el procesamiento interactivo con Colab descarga de torrents o intercambio de archivos entre pares utilización de un escritorio remoto o SSH conexión a proxies remotos minería de criptomonedas ejecución de ataques de denegación del servicio robo de contraseñas utilización de varias cuentas para evitar las restricciones de acceso o el uso de recursos creación de deepfakes ¿Cuál es la diferencia entre Jupyter y Colab? Jupyter es el proyecto de código abierto en el que se basa Colab. Colab te permite usar notebooks de Jupyter y compartirlos con otras personas sin tener que descargar, instalar ni ejecutar nada en la computadora. Cómo usar Colab ¿Dónde se almacenan mis notebooks y cómo puedo compartirlos? Los notebooks de Colab se almacenan en Google Drive, pero también se pueden cargar desde GitHub. Los notebooks de Colab se pueden compartir del mismo modo que las Hojas de cálculo o los Documentos de Google. Simplemente haz clic en el botón para compartir en la parte superior derecha de cualquier notebook de Colab, o bien sigue las instrucciones para compartir archivos de Google Drive.

話した内容Blog 今回は、10月の目標、アルゴリズムの乙女たち、Colaboratoryの有料プランの仕組みが変更となったので仕様を確認しました、Googleとnvidiaがそれぞれ3D生成の新モデル発表、今週の分析コンペ、雑談・来週話したいこと(AI雀荘) について話しました。 #regonn_curry_fm へのお便りはこちら https://forms.gle/BZsrPSa4znoQNfww8

About Sarah:Sarah Emanuel, one of BPC's School of Christian Learning teachers, holds a PhD with Distinction in Biblical Studies, with a graduate certificate in Women's and Gender Studies, from Drew University's Graduate Division of Religion. She received her M.A. in Religion from Wake Forest University, a graduate certificate in Ancient Jewish-Christian Encounters from Tel Aviv University International, and a B.A. in English and Liberal Studies from the University of Delaware, where she was inducted into Phi Beta Kappa and named a Woman of Promise. Prior to joining the LMU faculty, Professor Emanuel was Visiting Assistant Professor of Biblical Studies at Colby College (2018-2020) and Visiting Assistant Professor of New Testament at Oberlin College (2017-2018). Professor Emanuel's research attends to the Jewishness of Christian origins, the relationship among text, culture, and identity, and the interplay between traditional historical-critical methodologies and contemporary critical theory (e.g., queer theory, trauma theory, humor theory). She is co-chair for the CoLaboratory at Feminist Studies in Religion, Inc., where she co-hosts the podcast, "Feminists Talk Religion." She is also Content Area Editor of Biblical Studies at Ancient Jew Review. Some of Professor Emanuel's most recent publications include Humor, Resistance, and Jewish Cultural Persistence in the Book of Revelation: Roasting Rome (Cambridge University Press, 2020), “Grace Be to You in the Presence of the Past: Ghosts, Hauntings, and Traumatic Dissociations in Margaret Atwood's Alias Grace and the Gospel of John” (Gorgias Press, 2020), and “On the Eighth Day, God Laughed: ‘Jewing' Humor and Self-Deprecation in the Gospel of Mark and Crazy Ex-Girlfriend” (Journal of Modern Jewish Studies, 2020). Professor Emanuel is Slytherin Sun Hufflepuff Rising. When she's not teaching or researching, she can be found training, surfing, cello-ing, and exploring California with her partner, Zoë, and their three best fluffs: Gus, Doug, and Finn.How to follow Sarah:Follow Sarah on Twitter Link to her book on AmazonJoin us each Sunday from 8:45am-9:45am PSTSchool of Christian LearningYOUTUBEPlease consider supporting our show - https://www.bpcusa.org/financial-ministry/Support the show

R is the 18th level of the Latin alphabet. It represents the rhotic consonant, or the r sound. It goes back to the Greek Rho, the Phoenician Resh before that and the Egyptian rêš, which is the same name the Egyptians had for head, before that. R appears in about 7 and a half percent of the words in the English dictionary.  And R is probably the best language out there for programming around various statistical and machine learning tasks. We may use tools like Tensorflow imported to languages like python to prototype but R is incredibly performant for all the maths. And so it has become an essential piece of software for data scientists.  The R programming language was created in 1993 by two statisticians Robert Gentleman, and Ross Ihaka at the University of Auckland, New Zealand. It has since been ported to practically every operating system and is available at r-project.org. Initially called "S," the name changed to "R" to avoid a trademark issue with a commercial software package that we'll discuss in a bit. R was primarily written in C but used Fortran and since even R itself.  And there have been statistical packages since the very first computers were used for math.  IBM in fact packaged up BMDP when they first started working on the idea at UCLA Health Computing Facility. That was 1957. Then came SPSS out of the University of Chicago in 1968. And the same year, John Sall and others gave us SAS, or Statistical Analysis System) out of North Carolina State University. And those evolved from those early days through into the 80s with the advent of object oriented everything and thus got not only windowing interfaces but also extensibility, code sharing, and as we moved into the 90s, acquisition's. BMDP was acquired by SPSS who was then acquired by IBM and the products were getting more expensive but not getting a ton of key updates for the same scientific and medical communities. And so we saw the upstarts in the 80s, Data Desk and JMP and others. Tools built for windowing operating systems and in object oriented languages. We got the ability to interactively manipulate data, zoom in and spin three dimensional representations of data, and all kinds of pretty aspects. But they were not a programmers tool. S was begun in the seventies at Bell Labs and was supposed to be a statistical MATLAB, a language specifically designed for number crunching. And the statistical techniques were far beyond where SPSS and SAS had stopped. And with the breakup of Ma Bell, parts of Bell became Lucent, which sold S to Insightful Corporation who released S-PLUS and would later get bought by TIBCO. Keep in mind, Bell was testing line quality and statistics and going back to World War II employed some of the top scientists in those fields, ones who would later create large chunks of the quality movement and implementations like Six Sigma. Once S went to a standalone software company basically, it became less about the statistics and more about porting to different computers to make more money.  Private equity and portfolio conglomerates are, by nature, after improving the multiples on a line of business. But sometimes more statisticians in various feels might feel left behind. And this is where R comes into the picture. R gained popularity among statisticians because it made it easier to write complicated statistical algorithms without learning an entire programming language. Its popularity has grown significantly since then. R has been described as a cross between MATLAB and SPSS, but much faster. R was initially designed to be a language that could handle statistical analysis and other types of data mining, an offshoot of which we now call machine learning. R is also an open-source language and as with a number of other languages has plenty of packages available through a package repository - which they call CRAN (Comprehensive R Archive Network). This allows R to be used in fields outside of statistics and data science or to just get new methods to do math that doesn't belong in the main language.  There are over 18,000 packages for R. One of the more popular is ggplot2, an open-source data visualization package. data.table is another that performs programmatic data manipulation operations. dplyr provides functions designed to enable data frame manipulation in an intuitive manner. tidyr helps create tidier data. Shiny generates interactive web apps. And there are plenty of packages to make R easier, faster, and more extensible. By 2015, more than 10 million people used R every month and it's now the 13th most popular language in use. And the needs have expanded. We can drop r scripts into other programs and tools for processing. And some of the workloads are huge. This led to the development of parallel computing, specifically using MPI (Message Passing Interface).  R programming is one of the most popular languages used for statistical analysis, statistical graphics generation, and data science projects. There are other languages or tools for specific uses but it's even started being used in those.  The latest version, R 4.1.2, was released on 21/11/01. R development, as with most thriving open source solutions, is guided by a group of core developers supported by contributions from the broader community. It became popular because it provides all essential features for data mining and graphics needed for academic research and industry applications and because of the pluggable and robust and versatile nature. And projects like tensorflow and numpy and sci-kit have evolved for other languages. And there are services from companies like Amazon that can host and process assets from both, both using unstructured databases like NoSQL or using Jupyter notebooks. A Jupyter Notebook is a JSON document, following a versioned schema that contains an ordered list of input/output cells which can contain code, text (using Markdown), formulas, algorithms, plots and even media like audio or video. Project Jupyter was a spin-off of iPython but the goal was to create a language-agnostic tool where we could execute aspects in Ruby or Haskel or Python or even R. This gives us so many ways to get our data into the notebook, in batches or deep learning environments or whatever pipeline needs to be built based on an organization's stack. Especially if the notebook has a frontend based on Amazon SageMaker Notebooks, Google's Colaboratory and Microsoft's Azure Notebook. Think about this. 25% of the languages lack a rhotic consonant. Sometimes it seems like we've got languages that do everything or that we've built products that do everything. But I bet no matter the industry or focus or sub-specialty, there's still 25% more automation or instigation into our own data to be done. Because there always will be.

話した内容Blog このポッドキャストでは、Kaggleを中心としたデータサイエンス・機械学習に関連する情報、仮想通貨、xR、新しい働き方等について配信していきます。 今回は、Appleの新製品発表会、Colaboratory pro+の定期実行、今週の分析コンペ、雑談・来週話したいこと(書籍 叱る依存がとまらない) について話しました。 #regonn_curry_fm へのお便りはこちら https://forms.gle/BZsrPSa4znoQNfww8

This week, Coral sits down with the 2021 SFDFF Co-Laboratory team of dance-based performance group Fog Beast and filmmaker Lindsay Gauthier. Directed by Melecio Estrella and Andrew Ward,  Fog Beast weaves social, physical and aural curiosities driven by the lived experiences of convening and creativity. Lindsay Gauthier is a San Francisco based filmmaker and is also the founding Creative Producer of the San Francisco Dance Film Festival's Co-Laboratory.In this episode, the team discusses their early collaborations as dancers in Scott Wells and Company and chart the trajectories of their artistic paths that would eventually cross again in the creation of the new CoLaboratory film, Wild. They also discuss the initial inspiration and research for the film and how their collaboration with Sonoma county conservation group LandPaths helped connect them to the land and deepen  the creative process.Wild will  premiere in the Bay Area Shorts Program at the Brava theater on October 24th as a part of the twelfth annual San Francisco Dance Film Festival. Visit sfdancefilmfest.org to learn morehttps://www.fogbeast.com/companyhttp://www.lindsaygauthier.com/Raptproductions.comhttps://www.landpaths.org/@fogbeast@lulutrublu@landpaths@sfdancefilmfestTranspire- Another new collaboration between Lindsay and Meleciohttps://www.youtube.com/playlist?list=PLyywKinrREUZdkXQk4447cN_KPhcCIbdJ

話した内容ScrapBox このポッドキャストでは、Kaggleを中心としたデータサイエンスに関連する情報を配信していきます。 今回は、Colaboratoryのtips、Axross、3月の目標、今週のKaggleについて話しています。 ※ 最後の俳句のコーナーで花粉症は季語ではないと話していますが、合本俳句歳時記 第四版(角川書店)では、春の季語である「杉の花」の子季語で「花粉症」「杉花粉」が掲載されていたので季語でした。

Hamlet Batista with Jason Barnard at Ungagged Los Angeles 2019 Hamlet Batista talks with Jason Barnard about what tricks can be played when working with Google Colaboratory. Google Colaboratory is like Google Sheets on steroids. Use it to save bucketloads of time and be terribly productive. Gerry White got very excited about the 6 use-cases Hamlet Batista gave in his talk. For example automating duplication, writing meta titles automatically, Andrea Volpini's dance with machines, mapping redirects, writing alt tags, finding content gaps. 

Hamlet Batista with Jason Barnard at Ungagged Los Angeles 2019 Hamlet Batista talks with Jason Barnard about what tricks can be played when working with Google Colaboratory. Google Colaboratory is like Google Sheets on steroids. Use it to save bucketloads of time and be terribly productive. Gerry White got very excited about the 6 use-cases Hamlet Batista gave in his talk. For example automating duplication, writing meta titles automatically, Andrea Volpini's dance with machines, mapping redirects, writing alt tags, finding content gaps. 

Google Colaboratory is like Google Sheets on steroids. Use it to save bucketloads of time and be terribly productive. Gerry White got very excited about the 6 use-cases Hamlet gave in his talk. For example automating duplication, writing meta titles automatically, Andrea Volpini’s dance with machines, mapping redirects, writing alt tags, finding content gaps

Welcome to AI Club L.A. Podcast 2. In this podcast we will discuss Googles Colaboratory. Googles Colaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud. What is a Jupyter notebook environment? The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Let’s call it Colab for short. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. We will be using Colab in this podcast. Go to https://colab.research.google.com take a look around! Open an account with your Gmail, and start kicking the tires. Watch the three-minute Colaboratory introductory video.

Founder of Ballet co.Laboratory Zoé Henrot talks about the company's upcoming presentation of Remembering The Little Prince, featuring music of Camille Saint-Saëns, this weekend.

This week at The Story Collider, we're presenting two stories about confronting death. Part 1: Science communicator Anthony Morgan receives an invitation to be vacuum-sealed to the bottom of a helicopter -- for science! Part 2: As a medical student, Elorm Avakame befriends a patient who is dying from alcoholism. Anthony Morgan is the Creative Director of Science Everywhere!, an organisation devoted to adult science entertainment. The mission is to build science culture through engaging science entertainment for TV, youtube and live events. He's also on the board of a makerspace (Site 3 CoLaboratory) and has a recurring segment on Daily Planet. His background is in neuroscience/psychology and science communication, but he fell in love with science working at the Ontario Science Centre. Since then he’s been finding as many ways and places to "mic drop science" as he can. Elorm F. Avakame is a Pediatric resident physician at Children's National Medical Center in Washington, DC. He previously earned a Doctor of Medicine from Harvard Medical School and a Master's of Public Policy from the Harvard John F. Kennedy School of Government. He was also a Sheila C. Johnson Leadership Fellow at Harvard Kennedy School's Center for Public Leadership. Elorm is passionate about health issues affecting children in urban communities and wants to make life better for children on the margins. Learn more about your ad choices. Visit megaphone.fm/adchoices

Taro Minowa さんをゲストに迎えて、マラソン、OKR, 機械学習、ドラえもん、Skyrim などについて話しました。 Show Notes YAPC::Okinawa 2018 ONNASON Bay To Breakers Why I Quit Google to Work for Myself Error Budget Colaboratory Python — Homebrew Ruby25 映画ドラえもん のび太の宝島 ドラえもん物語 ～藤子・F・不二雄先生の背中～ | むぎわらしんたろう 【ちゃぶ台アフロ】スカイリム編 第1回【The Elder Scrolls V: Skyrim®】

Derek Quenneville of Site 3 coLaboratory demonstrates how 3D printers work and what can kids can learn by using them.