Podcasts about ted dunning

Welcome to another episode of Develomentor. Today's guest is Michael Hausenblas. BiographyMichael started his career in academia as a PhD academic researcher. These days, Michael is a Developer Advocate at AWS. He is part of the container service team, focusing on container security. Michael shares his experience around cloud-native infrastructure and apps through demos, blog posts, books, and public speaking engagements as well as contributes to open-source software. Before AWS, Michael worked at Red Hat, Mesosphere,MapR and in two research institutions in Ireland and Austria.Episode Summary“I found that industry overall is way less stressful than academic research. If it’s weekend or if I’m on PTO or whatever, I’m offline.““I’m a developer advocate. Basically, it's kind of like a guinea pig. We’re the first ones who get to play around with software and give feedback. It’s like, the UX here is great or here the UX sucks. So we are guinea pigs representing for the customer.”“I just love to be on-site with customers discussing things, trying things. The greatest part is being with actual users of the software that you’re producing and going over that in 400 level details.”—Michael HausenblasKey MilestonesWhat led Michael into academia and academic research at the start of his career?After about 4 years doing research focused work, Michael left for a startup and took a role doing pre-sales work. That’s a pretty far cry from doing research. What led to that change and what went into his decision process? What does a sales engineer do?Now Michael is in a dev advocate role, how does that compare with being a sales engineer?What attracted Michael to more public-facing roles like developer advocacy?Who is Ted Dunning? And why was he such a big influence for Michael?Additional ResourcesConnect with Ted Dunning on LinkedIn – https://www.linkedin.com/in/teddunning/Support Us at Develomentor – https://develomentor.com/support-us/You can find more resources in the show notesTo learn more about our podcast go to https://develomentor.com/To listen to previous episodes go to https://develomentor.com/blog/Follow Michael HausenblasLinkedIn: linkedin.com/in/mhausenblas/Twitter: @mhausenblasFollow Develomentor:Twitter: @develomentorFollow Grant IngersollTwitter: @gsingersLinkedIn: linkedin.com/in/grantingersoll

On this episode of the Early Adopter Research (EAR) Podcast, “Designing Enterprise Platforms,” EAR’s Dan Woods spoke with MapR’s CTO Ted Dunning. Designing Enterprise Platforms is a podcast about the deeper issues involved in creating big victories in enterprise technology. MapR is a company that has been around for most of the Hadoop era and has always taken the point of view that Hadoop was a great infrastructure that needed to be improved. MapR makes these improvements through the file system and then layering on a variety of other components. Their conversation covered: * 3:55 - The vision for MapR * 9:00 - What executives shouldn't know about their data systems * 14:30 - Why naming data is so important * 21:00 - Why data logistics are easier with a data fabric

欢迎收听丽莎老师讲机器人，想要孩子参加机器人竞赛、创意编程、创客竞赛的辅导，找丽莎老师！欢迎添加微信号：153 5359 2068，或搜索微信公众号：我最爱机器人。丽莎老师讲机器人之最受欢迎的十本人工智能书籍。1. Artificial Intelligence（人工智能）（第二版）作者：Stuart Jonathan Russell, Peter Norvig本书是一本介绍人工智能的指南，涵盖了人工智能的大半部分分支，包括智能代理、问题解决、逻辑代理、机器人等。本书对人工智能的发展历史做了介绍，包括人工智能最初是以“问题-解决”的模式出现的。本书有大量的在线样本代码，并以不同的语言编写。这是一本很棒的CS（计算机科学）书，里面有一些数学知识，但是附录中对这些数学背景进行了全面的补充。2. Neural Networks for Pattern Recognition（模式识别的神经网络）作者：Christopher M. Bishop这是从统计模式识别的角度，对前馈神经网络进行了首次综合整理。在对模式识别的基本概念进行过介绍后，介绍了建模概率密度函数的技术，并讨论了多层感知器和径向基函数网络模型的性能和相对优点。它还激励了对各种形式的错误函数的使用，并回顾了主要的使错误函数最小化的算法，总结了贝叶斯技术及其在神经网络中的广泛应用。3. Design Patterns in Java（Java中的设计模式）作者：Steven John Metsker, William C. Wake本书给你动手实践的机会，使你深刻理解在任何一项Java软件项目中，充分利用设计模式可发挥的重要作用。作为对经典设计模式的完美补充，本书集最新的Java特性和最佳实践于一身。4. Natural Language Processing with Python（使用Python进行自然语言处理）作者：Steven Bird, Ewan Klein, Edward Loper这本书提供了一个很容易理解的自然语言处理介绍，支持多种语言技术，从预测文本和电子邮件过滤到自动摘要和翻译。5. Mahout in Action（Mahout实战）作者：Sean Owen, Robin Anil, Ted Dunning本书通过使用Apache Mahout提供了关于机器学习的信息，包括使用组数据进行个别建议、查找逻辑集群和筛选分类等主题。6. Machine Learning（机器学习）作者：Kevin P. Murphy本书是一本关于机器学习的综合书。本书以一种统一的、概率性的方法，为机器学习领域提供了全面而独立的介绍，适用于具有入门级数学背景的高年级本科生和研究生。7. Lisp（ Lisp语言）（第三版）作者：Patrick Henry Winston, Berthold Horn新版本保留了之前版本的内容，又加入许多人工智能技术先进成果。本书从基本的语言到详细的例子，通过实践介绍Lisp。同时，还介绍了Common Lisp的对象系统，以及面向对象编程支持的提高生产力的技术。并介绍了专家系统、自然语言接口、符号数学等应用实例，以及概率边界处理、项目仿真和可视化对象识别等新应用。7. Practical Common Lisp（实用 Common Lisp）作者：Peter Seibel这是第一本将Common Lisp作为非学术语言介绍的书，提供了对Common Lisp的详细介绍，并提供了对Common Lisp语言特性及其工作原理的全面了解。8. Logical Foundations of Artificial Intelligence（人工智能逻辑基础）作者：Michael R. Genesereth本书从逻辑角度出发，是人工智能学科领域一本清晰、严谨、全面的基础书籍，是高等本科生和研究生的教科书，也是人工智能研究人员和开发人员的关键参考作品。9. Linux Robotics（Linux机器人）作者：D. Jay Newman这本书结合了机器人技术和编程中最复杂的部分，以填补现有信息的空白。如今，大多数机器人书籍都将微控制器作为机器人的“大脑”。这种方法适用于较小的、不太昂贵的项目，但有严重的局限性。当试图制造一个具有复杂动作能力、导航能力、视觉和图像捕捉能力的机器人时，最好使用单板计算机(SBC)，如Linux作为控制器。10.Pattern classification（模式分类）这本书全面地介绍了模式分类的基础理论和应用场景。

We're back with another edition of the Early Adopter Research Podcast. In this episode, Dan woods speaks with Ellen Friedman, a principal technologist and Ted Dunning, Chief Application Architect at MapR Technologies. Dunning and Friedman have recently completed books, and the conversation covers their insights on when it makes sense to create your own AI application and when it's preferable to use products offered by external vendors. The discussion also covers:

For this bonus episode, enjoy our recent interview with Ted Dunning, Apache Software Foundation board member, and the Chief Application Architect at MapR. © Gestalt IT, LLC for Gestalt IT: BONUS: Ted Dunning – IT Origins

Data experts Ted Dunning and Ellen Friedman discuss how data is used in today's digital society. They examine the enormous potential offered by big data, explain the new technologies that enable real time analytics and explore best practices for transforming how we plan large-scale computing projects.

Data experts Ted Dunning and Ellen Friedman discuss how data is used in today's digital society. They examine the enormous potential offered by big data, explain the new technologies that enable real time analytics and explore best practices for transforming how we plan large-scale computing projects.

Current Events; "Joe Lieberman" interview; Ted Dunning of KDVS's This Week in Science show

Current Events; "Joe Lieberman" interview; Ted Dunning of KDVS's This Week in Science show