We Have a Real Expert on AI and Big Data And We Take The Opportunity To Ask Him A Number of Questions About The Whole Area of AI This Week Show's Sponsors Kinsta: https://kinsta.com/ LaunchFlows: https://launchflows.com/ Matthew Renze We Interview Data Science Consultant, Author and Public Speaker Matthew Renze is a data science consultant, author, and public speaker. Over the past two decades, he’s taught over 300,000 software developers and IT professionals. He’s delivered over 100 keynotes, presentations, and workshops at conferences on every continent in the world (including Antarctica). His clients range from Fortune 500 companies to small tech startups around the globe. Matthew is a Microsoft MVP in AI, an ASPInsider, and an author for Pluralsight, Udemy, and Skillshare. He’s also an open-source software contributor. His focus includes artificial intelligence, data science, and machine learning https://matthewrenze.com/ The Main Topics For The Interview 1 - What is the main difference between human and machine learning and AI? #2 - What are Neuron Networks? #3 - How does "Weights" work connected to artificial Intelligence neuron networks? #4 - What does the term " BackPropagation" mean connected to AI? #5 - Do we fully understand the mathematics that is the basis of computer "Neuron Networks"? #6 - Did "Game Theory" has a key part in the development of modern AI? #7 - Is it true that most AI networks have about 300 neurons where the human brain has about 100,000,000,000 neurons? #8 - What are the most exciting development connected to AI? #9 - Is one of the real strengths of AI networks is speed compared to the human brain? #10 - Is it true that we don't have a clear understanding connected to how human beings learn and what is real human intelligence? #11 - Can you give some background on the noise problem that a lot of AI systems have connected to recognizing slight chances in images? Main Host Jonathan Denwood https://www.wp-tonic.com Co-Host Steven Sauder https://zipfish.io
Our Careers & Ai - a conversation with Matthew Renze
Matthew is an expert and has done tons of research on AI and all its developments. I was lucky to meet him at the beginning of 2020. A friend of ours, Jorge, organized a Conference in WeWork Buenos Aires. My Gosh! This seems ages ago due to the Pandemic and the endless days we are all facing all over the world. Together with his wife @Heather Wilde and a group of super technological trailblazers they were returning from The Antarctic Conference and made a stop in Buenos Aires, just before leaving for the United States. The talks were amazing. You can find the link below to watch and listen to them. Matthew speaks on AI in different parts, you can find him in Pluralsight, Udemy, among many more online places. Though we are all living in an AI dominant environment, with plenty of technology all around, there is a lot to do as humans. Do not get discouraged. Your touch, your human touch cannot be done, yet, by a machine. Tune in at your favorite platform! Let us all learn from Matthew. If you like this, please share this episode with your friends. Follow Matthew: https://matthewrenze.com/ https://renzeconsulting.com/ https://matthewrenze.com/contact/ Follow us: Website Twitter: @liftvalue Instagram: @liftvaluetranslations LinkedIn: Lift Value Translations & Consulting Youtube
Episode 151 – Artificial Intelligence with Matthew Renze
The 6 Figure Developer Podcast
We’re talking Artificial Intelligence with Matthew Renze! Matthew is a data science consultant, author, and international public speaker. He has over 2 decades of professional experience working with tech startups to Fortune 500 companies. He is a graduate of Iowa State University with double degrees in Computer Science and Philosophy, with a minor in Economics, and a focus on Artificial Intelligence and Machine Learning. His current focus is teaching others artificial intelligence, data science, and machine learning. What is AI Isnt this just another AI fad General vs Narrow What are the types of AI? Data science Machine learning Deep learning Reenforcement learning Is this data about AI growth coming from humans or AI? Are we already being influenced/controlled – All hail the basalisk! What kinds of things will AI be doing/helping with Are we all going to lose our jobs? No, you’ll just have to know how to clean up the mess Is my job at risk? AI Career path AI Trainer AI Developer AI User Invest in AI Education/Career Assets Economy Ethical Issues Facial Recognition Bias Transparency What comes next? Skynet Links https://matthewrenze.com https://twitter.com/matthewrenze https://www.linkedin.com/in/matthewrenze/ https://www.pluralsight.com/authors/matthew-renze Resources Artificial Intelligence: Preparing Your Career for AI Natural Language Bash “Tempting Time” by Animals As Leaders used with permissions – All Rights Reserved × Subscribe now! Never miss a post, subscribe to The 6 Figure Developer Podcast! Are you interested in being a guest on The 6 Figure Developer Podcast? Click here to check availability!
Matthew Renze is a data science consultant, author, and public speaker. Over the past two decades, he has taught over 200,000 software developers and IT professionals how to make better decisions with data science. His clients include small software startups to Fortune 100 companies around the globe. I recently joined him on an adventure mastermind trip to Antarctica (see AntarctiConf.com). In this interview, learn about his journey in becoming a data science consultant. Read his articles on data science and watch his free courses at www.matthewrenze.com Christina Aldan is the CEO/founder of LG Designs (lgdesigns.co) and a digital media marketer who travels the world speaking to tech industry folks, entrepreneurs, and female business owners
Matthew Renze on Data Science for Developers - Episode 44
Azure DevOps Podcast
Jeffrey’s guest today is Matthew Renze. Matthew is a Data Science Consultant, author, and public speaker. Over the past two decades, Matthew has taught over 200,000 developers and IT professionals how to make better decisions with data science! His clients include small software startups to fortune 100 companies across the globe. He’s also a Microsoft MVP, an ASPInsider, a Pluralsight author, and an open-source software contributor. His focus includes data science, machine learning, and artificial intelligence. In this week’s episode, Jeffrey and Matthew are discussing data science for developers. Matthew explains what data science is, what developers should be aware of, the powerful ways in which data science can be leveraged, real-world examples of how software developers can use data science, the difference between machine learning and data science, and what’s available right now for developers who want to use utilize data science today. Topics of Discussion: [:38] Be sure to visit AzureDevOps.Show for past episodes and show notes! [:53] Where to find Jeffrey’s book, .NET DevOps for Azure. [1:32] About today’s episode and guest. [2:07] Jeffrey welcomes Matthew to the show! [2:25] Matthew speaks about his career journey and how he has ended up where he is today. [6:25] What is data science? And what should developers be aware of? [9:13] The powerful ways in which data science can be used. [11:22] Matthew provides some real-world examples of how software developers can use data science. [14:16] What’s the difference between machine learning and data science? And how do they fit together? [16:43] A word from Azure DevOps sponsor: Clear Measure. [17:10] Matthew explains what software developers can do with what’s available today in data science. [20:26] If developers want to utilize data science, would they need to design their own data repository? [21:21] What are the common choices for storing the data you gather? [22:49] Is data science just a further progression beyond Kimball methods of star schemas and data warehousing? Or is it something completely different? [23:46] Matthew explains some of the common terms associated with data science. [28:26] What does a DevOps pipeline look like for data science? What does it look like to deploy a database? [30:06] Where does A.I. fit into all of this? [34:03] Does Matthew see this use of data science as a whole different paradigm shift to thinking? [36:36] Resources Matthew recommends listeners follow-up on after this week’s episode. [37:40] Where to learn more about Matthew and his resources online! Mentioned in this Episode: Azure DevOps Clear Measure (Sponsor) — Reach out to Jeffrey @JeffreyPalermo on Twitter if you have a user group or conference and would like some free copies of .NET DevOps for Azure! .NET DevOps for Azure, by Jeffrey Palermo bit.ly/dotnetdevopsproject Microsoft Build Conference Matthew Renze ASPInsiders Pluralsight.com/Authors/Matthew-Renze Matthew Renze’s Microsoft MVP Profile Azure Application Insights Python R (Programming Language) Star schema “Getting Started with Data Science,” by Matthew Renze Matthew Renze’s Twitter: @MatthewRenze Matthew Renze’s LinkedIn Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.
Herding Code 235: Matthew Renze on Data Science for Software Developers
Download / Listen: Herding Code 235: Matthew Renze on Data Science for Software Developers https://herdingcode.com/wp-content/uploads/HerdingCode-0235-Matthew-Renze.mp3 At DevSum Stockholm, Jon talks to Matthew Renze (@matthewrenze ) about data science practices to improve both the products they are creating and their software development practices. Topics: (00:20) Matthew explains how he’s been speaking to software developers about applying data science practices to improve both the products they are creating and their software development practices.(00:40) Data science can add intelligence to applications, machine learning to automate decision-making processes, and deep learning to modify the user interface using anticipatory design.(03:57) The other side to this is using data science to help build software. The DevOps pipeline provides a lot of objective measures to help improve our software development processes and practices.(05:51) Software telemetry data can help us prioritize the time we spend on features towards those that are actively used.(07:12) Jon asks which terms he really needs to understand as a developer. Matthew defines data science, machine learning, deep learning, and reinforcement learning. They discuss how text suggestions and language understanding have progressed, and where generated text can and can’t help.(13:55) Machine learning can be used for good and for evil – for instance, it’s now possible to forge video in a way that’s really tough to detect. What do we do now? Matthew talks about what we can do as developers to educate those around us and apply ethics to the software we contribute to.(19:50) How do we handle things like legal liability for machines that are making decisions, like self-driving cars? Matthew puts it in historical context and talks about how we’ll need to adapt our society to accommodate.(24:12) Jon asks where to get started applying data science today. Matthew gives some pointers on where to get started learning, and how to start with some quick wins like A/B testing and objective software quality metrics.