Podcasts about machine learning algorithms

Ryan returns with the second installment of his AI-focused series, diving into key terminology, must-know platforms, and actionable insights for home service businesses. Whether you're just starting with AI or looking to deepen your understanding, this episode lays a solid foundation and sets the stage for next week's deep dive into crafting effective AI prompts. Discover the meaning of terms like GPT, machine learning, and AGI, and learn how platforms like ChatGPT, Claude, and MidJourney can transform your business operations and marketing strategies. Ryan also shares the importance of embracing AI now to stay competitive in the rapidly evolving landscape. 1. Why AI Matters: Recap of last week's AI introduction. Why businesses that fail to adopt AI could become obsolete within 5-8 years. The disproportionate advantage of using AI for efficiency, growth, and innovation. 2. Breaking Down AI Jargon: GPT (Generative Pre-trained Transformer): What it means and how it works. AI (Artificial Intelligence): A broad overview of intelligent machines. Machine Learning: Algorithms that learn patterns from data. Deep Learning: Advanced neural networks for image recognition, spam detection, and more. AGI (Artificial General Intelligence): The cutting edge of AI and its potential risks. 3. Overview of Popular AI Platforms: Ryan provides a quick breakdown of leading platforms and their strengths: ChatGPT: The all-purpose tool with customizable GPTs for business and personal use. Google Bard: Google's AI response tool and its integration with search results. Microsoft Bing Chat: Powered by GPT with unique capabilities tied to Microsoft's ecosystem. MidJourney & DALL-E: AI tools for creating stunning images and marketing visuals. Claude: A favorite for marketing agencies, offering project-based AI insights. Perplexity: Ideal for finding historical data with extended search capabilities. Jasper: A marketing-focused AI platform tailored for branding and content creation. 4. Real-Life AI Applications: Using ChatGPT and Claude for operational efficiencies like contract reviews and SOPS. How MidJourney sparks creative ideas for logos, social media content, and more. Automating customer interactions with AI schedulers and CRM integrations. 5. Preparing for Next Week's Episode: Teaser for the next podcast on building effective prompts to get the best results from AI. How learning to craft precise prompts can maximize AI's potential in your business. Ryan encourages listeners to embrace AI now, highlighting its potential to redefine business operations, marketing, and growth strategies. With tools that reduce manual effort and increase productivity, AI is a game-changer for home service businesses. Tune in next week for a hands-on guide to crafting prompts that unlock AI's full potential. Make sure to watch the episode on YouTube for live demonstrations and practical takeaways. Stay ahead of the curve—don't just listen, take action! Learn more about Garage Door U Summit 2025 at: https://garagedoorsummit.com/ Find Ryan at: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://garagedooru.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aaronoverheaddoors.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠https://markinuity.com/⁠ Check out our sponsors! Sommer USA - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://sommer-usa.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Surewinder - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://surewinder.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Stealth Hardware - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://quietmydoor.com/⁠

Look at what Santa dropped when he came down the chimney last night. A bunch of valuable ThisISResearch Best paper Awards! As we do at the end of every year, we look back at the finest information systems scholarship our field has produced this year, and we pick some of our favorite papers that we want to give an award too. Like in previous years, we recognize three different kinds of best papers – a paper that is innovative in its use of research methods, a paper that is a fine example of elegant scholarship, and a paper that is trailblazing in the sense that it starts new conversations in our field. References Pujol Priego, L., & Wareham, J. (2023). From Bits to Atoms: White Rabbit at CERN. MIS Quarterly, 47(2), 639-668. Recker, J., Zeiss, R., & Mueller, M. (2024). iRepair or I Repair? A Dialectical Process Analysis of Control Enactment on the iPhone Repair Aftermarket. MIS Quarterly, 48(1), 321-346. Seidel, S., Frick, C. J., & vom Brocke, J. (2025). Regulating Emerging Technologies: Prospective Sensemaking through Abstraction and Elaboration. MIS Quarterly, 49, . Abbasi, A., Somanchi, S., & Kelley, K. (2025). The Critical Challenge of using Large-scale Digital Experiment Platforms for Scientific Discovery. MIS Quarterly, 49, . Lindberg, A., Schecter, A., Berente, N., Hennel, P., & Lyytinen, K. (2024). The Entrainment of Task Allocation and Release Cycles in Open Source Software Development. MIS Quarterly, 48(1), 67-94. Kitchens, B., Claggett, J. L., & Abbasi, A. (2024). Timely, Granular, and Actionable: Designing a Social Listening Platform for Public Health 3.0. MIS Quarterly, 48(3), 899-930. Chen, Z., & Chan, J. (2024). Large Language Model in Creative Work: The Role of Collaboration Modality and User Expertise. Management Science, 70(12), 9101-9117. Matherly, T., & Greenwood, B. N. (2024). No News is Bad News: The Internet, Corruption, and the Decline of the Fourth Estate. MIS Quarterly, 48(2), 699-714. Morse, L., Teodorescu, M., Awwad, Y., & Kane, G. C. (2022). Do the Ends Justify the Means? Variation in the Distributive and Procedural Fairness of Machine Learning Algorithms. Journal of Business Ethics, 181(4), 1083-1095. Hansen, S., Berente, N., & Lyytinen, K. (2009). Wikipedia, Critical Social Theory, and the Possibility of Rational Discourse. The Information Society, 25(1), 38-59. Habermas, J. (1984). Theory of Communicative Action, Volume 1: Reason and the Rationalization of Society. Heinemann.   

Welcome to the NeurologyLive® Mind Moments® podcast. Tune in to hear leaders in neurology sound off on topics that impact your clinical practice. In this episode, Lara Jehi, MD, MHCDS, an epilepsy specialist and Cleveland Clinic's Chief Research and Information Officer, sat down to discuss a recently published study that explored using machine learning algorithms to predict seizure control after epilepsy surgery. In the interview, Jehi explained the unique aspects of the study design, emphasizing the importance of a large, well-characterized patient cohort with consistent follow-up and the choice of scalp EEG—a commonly used, non-invasive test in epilepsy care—as the data source. In addition, Jehi touched on the use of AutoML to streamline the process, enabling efficient identification of the top-performing algorithms and enhancing the model's predictive accuracy. Furthermore, she spoke on the team needed to properly implement machine learning techniques for neurosurgery, while providing recommendations for other institutions interested in pursuing these types of approaches. Looking for more epilepsy discussion? Check out the NeurologyLive® epilepsy clinical focus page. Episode Breakdown: 1:00 – Background on various machine learning approaches for epilepsy research 3:20 – Study details, findings, and notable takeaways 8:20 – Neurology News Minute 10:20 – Novelty in using scalp EEG and its global application 15:30 – Team personnel needed for proper implementation of machine learning techniques in epilepsy surgery The stories featured in this week's Neurology News Minute, which will give you quick updates on the following developments in neurology, are further detailed here: FDA Accepts Resubmitted NDA for Ataluren in Nonsense Duchenne Muscular Dystrophy FDA Places Clinical Hold on Epilepsy Agent RAP-219 for Diabetic Peripheral Neuropathic Pain First-Ever CRISPR/Cas13-RNA Editing Therapy to be Tested in Phase 1 Study of Age-Related Macular Degeneration Thanks for listening to the NeurologyLive® Mind Moments® podcast. To support the show, be sure to rate, review, and subscribe wherever you listen to podcasts. For more neurology news and expert-driven content, visit neurologylive.com.

Today, you'll learn about a record-breaking deep drill into the Earth's mantle, the new science behind the ancient Chinese diagnostic practice of tongue examination, and how a new discovery at Stonehenge is opening up yet more mysteries. Mantle Drill “Geologists drill 1.2 km into rare rocks from Earth's mantle.” by Michael Irving. 2024. “Internal Structure of Earth: Crust, Mantle & Core, Discontinuities.” Rau's IAS. 2024. “Earth's layers: Exploring our planet inside and out.” by Daisy Dobrijevic. 2023. “A long section of serpentinized depleted mantle peridotite.” by C. Johan Lissenberg, et al. Tongue Exam “Say ‘aah' and get a diagnosis on the spot: is this the future of health?” University of South Australia. 2024. “Tongue Disease Prediction Based on Machine Learning Algorithms.” by Ali Raad Hassoon, et al. 2024. Stonehenge Discovery “Stonehenge's Strangest Rock Came From 500 Miles Away.” by Meghan Bartels. 2024. “A Scottish provenance for the Altar Stone of Stonehenge.” by Anthony J. I. Clarke, et al. 2024. Follow Curiosity Daily on your favorite podcast app to get smarter with Calli and Nate — for free! Still curious? Get exclusive science shows, nature documentaries, and more real-life entertainment on discovery+! Go to https://discoveryplus.com/curiosity to start your 7-day free trial. discovery+ is currently only available for US subscribers. Hosted on Acast. See acast.com/privacy for more information.

Michele Tucci, the Chief Strategy Officer and Managing Director for Americas at Credolab, a groundbreaking fintech company revolutionizing credit risk assessment. In this episode, Michele shares his incredible journey in the industry and how Credolab utilizes smartphone metadata analysis to provide financial inclusion.Discover the three game-changing insights they discuss:1️⃣ Unveiling the Secret: How Smartphone Metadata Analysis is Transforming Credit Risk Assessment2️⃣ The Shocking Truth: Traditional Credit Assessment Methods are Obsolete3️⃣ The Future is Here: Harnessing the Power of Machine Learning Algorithms for Accurate Creditworthiness EvaluationTune in to this episode to gain exclusive access to Michele's expertise and learn how Credolab is reshaping the financial landscape with data privacy and security at the forefront.Also, watch the entire episode on youtube. Links:CredolabWebsite: https://www.credolab.com/Linkedin: https://www.linkedin.com/company/credolab/X (Twitter): https://twitter.com/CredoLabFacebook: https://www.facebook.com/credolab/Fintech Confidential YouTube: https://fintechconfidential.com/watch Podcast: https://fintechconfidential.com/listen Notifications: https://fintechconfidential.com/accessLinkedIn: https://www.linkedin.com/company/fintechconfidentialTwitter: https://twitter.com/FTconfidential Instagram: https://www.instagram.com/fintechconfidential Facebook: https://www.facebook.com/fintechconfidentialSupportersSupport is provided by MPC 2023, the premier event for fintech leaders. This is your chance to shake hands and rub shoulders with the world's top experts in payments, loyalty, blockchain, digital currencies, cybersecurity, consumer privacy, and other emerging fintech solutions connecting you directly with the future of commerce. If you haven't already, mark your calendars for August 23rd through the 25th and join me and Fintech Confidential at the Westin Atlanta Perimeter North. When you sign up for FinTech confidential notifications, you will receive a discount from $50 to 100% off.Or use this link for $50 of registration. https://mpcevent.com/FTC50Time Stamps:[00:10:14] Why Listening to Clients is Crucial for Product DevelopmentFind out why listening to clients and their feedback is essential in shaping the development of a product.[00:06:13] The Fintech Industry: What's Changing?Discover how the fintech industry is evolving differently in developed countries and emerging markets, and what it means for you.[00:13:26] Smartphone Metadata Analysis: A Game-Changer for Credit Risk AssessmentLearn how Credo Labs is revolutionizing credit risk assessment by using smartphone metadata analysis.[00:14:55] Financial Inclusion: Solving the Problem of the UnbankedExplore the potential of using smartphone data for credit risk assessment and how it can help address financial exclusion.[00:16:39] Breaking the Mold: Changing Credit Assessment in Financial InstitutionsFind out how Credo Labs is disrupting traditional underwriting processes and providing access to...

In this episode of The AI Frontier, we explore the emerging role of AI in healthcare. Join us as we discuss the history of AI in healthcare, the current applications of AI in medical diagnosis and treatment, and the ethical considerations and challenges associated with its use. We also take a look into the future of AI in healthcare, and what listeners can expect to see in the years to come. Tune in to learn more about the AI Doctor - Fact or Fiction?Support the Show.Keep AI insights flowing – become a supporter of the show!Click the link for details

The Taiao programme, led by the University of Waikato, aims to create machine learning tools to help researchers wrangle with massive amounts of environmental data. Claire Concannon visits the team to learn more.

"From 100 Hour Weeks In a Wine Bar, to Machine Learning Algorithms for Real Estate, and Creating One of the Fastest Growth Tech Companies in Australia" On Episode 50 of the “Future of Australia” podcast, I speak with Richie Ragel and Michael Cleary the Co-Founders of Milk Chocolate Property, which grew 129% last year, to do over $1.9 million in annual revenue. This made Milk Chocolate Property, on the Financial Review list of 100 fastest growing new companies in Australia, one of the FASTEST GROWING new businesses in Australia.

If the machines have learned anything, it's that we're talking about them and machine learning's impact on our future.Watch the Undecided with Matt Ferrell episode, “How AI Could Solve Our Renewable Energy Problem”: https://youtu.be/HAdiVIitI9M?list=PLnTSM-ORSgi5LVxHfWfQE6-Y_HnK-sgXSYouTube version of the podcast: https://www.youtube.com/stilltbdpodcastGet in touch: https://undecidedmf.com/podcast-feedbackSupport the show: https://pod.fan/still-to-be-determinedFollow us on Twitter: @stilltbdfm @byseanferrell @mattferrell or @undecidedmfUndecided with Matt Ferrell: https://www.youtube.com/undecidedmf★ Support this podcast ★

Artificial intelligence (AI) and machine learning algorithms are transforming treatment and prognosis in hematological malignancies, as well as other areas... The post The impact of AI and machine learning algorithms in MDS appeared first on VJHemOnc.

Artificial intelligence (AI) and machine learning algorithms are transforming treatment and prognosis in hematological malignancies, as well as other areas... The post The impact of AI and machine learning algorithms in MDS appeared first on VJHemOnc.

In this episode I covered the USE CASE OF UPI- TRANSACTION What is Clustering? Clustering or cluster analysis is a machine learning technique, which groups the unlabelled dataset. It can be defined as "A way of grouping the data points into different clusters, consisting of similar data points K-means clustering is one of the simplest and popular unsupervised machine learning algorithms. ... In other words, the K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible. You can think of an N-gram as the sequence of N words, by that notion, a 2-gram (or bigram) is a two-word sequence of words like “please turn”, “turn your”, or ”your homework”, and a 3-gram (or trigram) is a three-word sequence of words like “please turn your”, or “turn your homework” check the episode on various platform https://www.instagram.com/podcasteramit Apple :https://podcasts.apple.com/us/podcast/id1544510362 Huhopper Platform :https://hubhopper.com/podcast/tech-stories/318515 Amazon: https://music.amazon.com/podcasts/2fdb5c45-2016-459e-ba6a-3cbae5a1fa4d Spotify :https://open.spotify.com/show/2GhCrAjQuVMFYBq8GbLbwa

In this episode, Antonia and Andrew discuss a selection of articles from the February 2, 2022 issue of JBJS, along with an added dose of entertainment and pop culture. Listen at the gym, on your commute, or whenever your case is on hold! Articles Discussed: Is Discretionary Care Associated with Safety Among Medicare Beneficiaries Undergoing Spine Surgery?, by Ko et al. Sex Differences in End-Stage Ankle Arthritis and Following Total Ankle Replacement or Ankle Arthrodesis, by Dodd et al. Development and Internal Validation of Machine Learning Algorithms for Predicting Hyponatremia After TJA, by Kunze et al. Redefining the 3D Topography of the Acetabular Safe Zone. A Multivariable Study Evaluating Prosthetic Hip Stability, by Hevesi et al. National Trends in Post-Acute Care Costs Following Total Hip Arthroplasty from 2010 through 2018, by Serino et al. Halter Traction for the Treatment of Atlantoaxial Rotatory Fixation, by Yeung et al. An Epidemic Amidst a Pandemic: Musculoskeletal Firearm Injuries During the COVID-19 Pandemic, by Inclan et al. Epidemiology, Treatment, and Treatment Quality of Overriding Distal Metaphyseal Radial Fractures in Children and Adolescents, by Laaksonen et al. Link: JBJS website: https://jbjs.org/issue.php Sponsor: This episode is brought to you by the Miller Review Course. Subspecialties: Spine Foot & Ankle Hip Knee Pediatrics Trauma

Happy New Year Everyone! Hope you all enjoyed the holiday period. I'm baaaack and in today's episode, I discuss a subcategory of machine learning - Supervised Learning. I break down the basics of Supervised Learning, what it is and how it works. Get in touch and let me know what you thought! Twitter: @Delicate_Data Email: timicode54@gmail.com --- Send in a voice message: https://anchor.fm/delicatedatabase/message

In this thought-provoking episode, Marc and Mo highlight a recent single-center study on the use of machine-learning algorithms in orthopaedics and use it as a springboard into a larger discussion of the inherent opportunities and challenges that have always been associated the ongoing process of scientific exploration into new domains, with a focus on the intriguing question: “Is there a day in the future when clinical decision-making will be reduced to these machine-learning algorithms?” OrthoJOE Mailbag: feedback, comments, and suggestions from our audience can be sent to orthojoe@jbjs.org Links: Kunze KN, Polce EM, Clapp I, Nwachukwu BU, Chahla J, Nho SJ. Machine Learning Algorithms Predict Functional Improvement After Hip Arthroscopy for Femoroacetabular Impingement Syndrome in Athletes. J Bone Joint Surg Am. 2021 Jun 16;103(12):1055-1062. doi: 10.2106/JBJS.20.01640. PMID: 33877058. https://jbjs.org/reader.php?id=209282&rsuite_id=2845874&native=1&source=The_Journal_of_Bone_and_Joint_Surgery/103/12/1055/fulltext&topics=sm#info OrthoBuzz Blog Entry: https://orthobuzz.jbjs.org/2021/06/21/use-of-machine-learning-to-predict-improvement-after-hip-arthroscopy/ Author Insights video: https://www.youtube.com/watch?v=KRT98A2HDn8

Gwen reveals how YouTube ranks your videos with its machine learning algorithms, and how this can help your YouTube channel succeed. HOSTS: The Video Marketing Value Podcast is hosted by:- Dane Golden of VidiUp.tv and VidTarget.io | LinkedIn | Twitter | YouTube- Gwen Miller  Hearst Magazines | LinkedIn | TwitterSPONSORS: This episode is brought to you by our affiliate partners, including: TubeBuddy, VidIQ, MorningFame, Rev.com, and other products and services we recommend.PRODUCER: Jason Perrier of Phizzy StudiosMORE INFO

Gwen reveals how YouTube ranks your videos with its machine learning algorithms, and how this can help your YouTube channel succeed. HOSTS: The Video Marketing Value Podcast is hosted by:- Dane Golden of VidiUp.tv and VidTarget.io | LinkedIn | Twitter | YouTube- Gwen Miller  Hearst Magazines | LinkedIn | TwitterSPONSORS: This episode is brought to you by our affiliate partners, including: TubeBuddy, VidIQ, MorningFame, Rev.com, and other products and services we recommend.PRODUCER: Jason Perrier of Phizzy StudiosMORE INFO

Looking for types of machine learning algorithms? We have written a detailed blog on machine learning algorithms types Listen Now. Types of Machine Learning Algorithms Machine Learning has evolved from a science fiction idea to the most accurate and diverse business method available, improving any business on multiple verticles. Its impact on the output of various businesses has grown to the point where high-quality machine learning algorithms are needed to ensure many industries' survival in this sector. Big Data applications would certainly play a significant role in potential technical advancements. Machine learning and artificial intelligence, on the other hand, are critical to unlocking the value of data. The following is a brief description of the relationship between the three on that big data is used for materials, machine learning is used as a tool, and artificial intelligence is the result. Echo innovate IT is a custom software development company delivering interactive and robust IT solutions across the globe, having years of experience in serving quality software development services. We are technical partners to our clients, develop long-term, equally helpful relationships, and ensure they get ideal returns on their IT investments. Our software development consultants consist of tech-savvy engineers, business analysts, software specialists, and project managers. --- Send in a voice message: https://anchor.fm/echo-innovate-it/message

For this week's Feature Discussion, please join authors Michael Ackerman, Christopher Haggerty, editorialist Michael Rosenberg, and Associate Editor Nicholas Mills as they discuss the original research articles “Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device,” “ Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead Electrocardiogram and Help Identify Those at Risk of AF-Related Stroke,” and “Trusting Magic: Interpretability of Predictions from Machine Learning Algorithms.”   TRANSCRIPT BELOW: Dr. Carolyn Lam: Welcome to Circulation on the Run, your weekly podcast summary and backstage pass to the journal and its editors. We're your cohosts. I'm doctor Carolyn Lam, associate editor from the National Heart Center and Duke National University of Singapore. Dr. Greg Hundley: And I'm Greg Hundley, associate editor, director of the Pauley Heart Center at VCU Health in Richmond, Virginia. Well Carolyn, this week's feature, it's kind of a new thing for us. It's more than our double feature; it's actually a forum, where we're going to have two papers discussed, we'll have both authors represented from each of those two papers, we'll have an editorialist, and we'll have one of our associate editors. And the topic, Carolyn, just to keep you in suspense, is really on machine learning and actually how that can be applied to 12 lead electrocardiograms. But before we get to that, how about we grab a cup of coffee and start off on some of the other articles in this issue? Would you like to go first? Dr. Carolyn Lam: Yes, I would, but you're really keeping me in suspense. But first, let's focus on health related quality of life. We know that poor quality of life is common in heart failure, but there are few data on heart health related quality of life and its association with mortality outside of the Western countries. Well, until today's paper. And it's from the Global Congestive Heart Failure, or GCHF study, the largest study that has systematically examined health-related quality of life as measured by the Kansas City cardiomyopathy questionnaire 12, or KCCQ, and its association with outcomes in more than 23,000 patients with heart failure across 40 countries, in eight major geographic regions, spanning five continents. Dr. Greg Hundley: Wow, Carolyn. That KCCQ 12, that has been such an interesting tool for us to use in patients with heart failure. So what did they find in this study? Dr. Carolyn Lam: Really important. So the health-related quality of life differs considerably between geographic regions with markedly lower quality of life related to heart failure in Africa than elsewhere. Quality of life was a strong predictor of death and heart failure hospitalization in all regions, irrespective of symptoms class, and in both preserved and reduced ejection fraction. So there are some important clinical implications, namely that health-related quality of life is an inexpensive and simple prognostic marker that may be useful in characterizing symptom severity and prognosis in patients with heart failure. And there is certainly a need to address disparities that impact quality of life in patients with heart failure in different regions of the world. Dr. Greg Hundley: Very nice, Carolyn. Well, I'm going to turn to the world of basic science and bring us a paper from David Merryman from Vanderbilt University. So Carolyn, myocardial infarction induces an intense injury response, which ultimately generates a collagen dominated scar. Cardiac myofibroblasts are the cells tasked with depositing and remodeling collagen and are a prime target to limit the fibrotic process post myocardial infarction. Now Carolyn, serotonin 2B receptor signaling has been shown to be harmful in a variety of cardiopulmonary pathologies, and could play an important role in mediating scar formation after MI. So Carolyn, these investigators employed two pharmacologic antagonists to explore the effect of serotonin 2B receptor inhibition on outcomes post myocardial infarction and characterized the histological and micro structural changes involved in tissue remodeling. Dr. Carolyn Lam: Oh, that's very interesting, Greg. What did they find? Dr. Greg Hundley: So Carolyn, serotonin 2B receptor antagonism preserved cardiac structure and function by facilitating a less fibrotic scar, indicated in their results by decreased scar thickness and decreased border zone area. Serotonin 2B receptor antagonism resulted in collagen fiber redistribution to a thinner collagen fiber. And they were more anisotropic. They enhanced left ventricular contractility and the fibrotic tissue stiffness was decreased, thereby limiting the hypertrophic response of the uninjured cardiomyocytes. Dr. Carolyn Lam: Wow. That is really fascinating, Greg. Summarize it for us. Dr. Greg Hundley: Yeah, sure. So this study, Carolyn, suggests that early inhibition of serotonin 2B receptor signaling after myocardial infarction is sufficient to optimize scar formation, resulting in a functional scar, which is less likely to expand beyond the initial infarct and cause long-term remodeling. The prolonged presence of the antagonist was not required to maintain the benefits observed in the early stages after injury, indicating that acute treatment can alter chronic remodeling. So Carolyn, it's really going to be interesting to see how this research question is pursued in studies of larger animals, including us, or human subjects. Dr. Carolyn Lam: Wow, that is really interesting. And so is this next paper. Well, we know that genetic variation in coding regions of genes are known to cause inherited cardiomyopathies and heart failure. For example, mutations in MYH7 are a common cause of hypertrophic cardiomyopathy, while mutations in LMNA are a common cause of dilated cardiomyopathy with arrhythmias. Now, to define the contribution of non-coding variations, though, today's authors, led by Dr. Elizabeth McNelly from Northwestern University Feinberg School of Medicine in Chicago and colleagues evaluated the regulatory regions for these two commonly mutated cardiomyopathy genes, namely MYH7 and LMNA. Dr. Greg Hundley: Wow, Carolyn. So this is really interesting. So how did they do this and what did they find? Dr. Carolyn Lam: You asked the top questions, because the method is just as interesting as the findings here. They used an integrative analysis that relied on more than 20 heart enhancer function and enhancer target datasets to identify MYH7 and LMNA left ventricular enhancer regions. They confirmed the activity of these regions using reporter assay and CRISPR mediated deletion of human cardiomyocytes derived from induced pluripotent STEM cells. These regulatory regions contained sequence variants within transcription factor binding sites that altered enhancer function. Extending the strategy genome-wide, they identified an enhancer modifying variant upstream of MYH7. One specific genetic variant correlated with cardiomyopathy features derived from biobank and electronic health record information, including a more dilated left ventricle over time. So these findings really link non-coding enhancer variation to cardiomyopathy phenotypes, and provide direct evidence of the importance of genetic background. Beautiful paper. Dr. Greg Hundley: Very nice, Carolyn. Dr. Carolyn Lam: But let me quickly tell you what else is in this issue. We have an ECG Challenge by Dr. Lutz on flash pulmonary edema in a 70-year-old; there's an On My Mind paper by Dr. Halushka, entitled (An) Urgent Need for Studies of the Late Effects of SARS-CoV-2 on the Cardiovascular System. Dr. Greg Hundley: Ah, Carolyn. Well, in the mailbox, there are two Research Letters, one from Dr. Soman entitled (The) Prevalence of Atrial Fibrillation and Thromboembolic Risk in Wild-Type Transthyretin Amyloid Cardiomyopathy, and a second letter from Dr. Berger entitled Multiple Biomarker Approaches to Risk Stratification in COVID-19. Well Carolyn, now let's get on to that forum discussion and hear a little bit more about using machine learning in the interpretation of a 12 lead ECG. Dr. Carolyn Lam: Wow, can't wait. Thanks, Greg. Dr. Greg Hundley: Well listeners, we are here today for a double feature, but this double feature is somewhat unique, in that we are going to discuss together two papers that focus on machine learning applications as they relate to the interpretation of the electrocardiogram. With us today, we have Mike Ackerman from Mayo Clinic, Chris Haggerty from Geisinger, Mike Rosenberg as an editorialist from University of Colorado, and then our own Nick Mills, an associate editor with Circulation. Welcome, gentlemen. Well, Mike Ackerman, we will start with you first. Could you describe for us the hypothesis that you wanted to test, and what was your study population and your study design? Dr. Michael Ackerman: Thanks, Greg. The hypothesis was pretty simple, and that is could an artificial intelligence based approach, machine learning, deep neural network, could that solve the QT problem? Which is one of the big secrets among cardiologists, which, as you know, one of your associate editors, Sammy Biskin, published a sobering paper over a decade ago, showing and revealing the secret that cardiologists are not so hot at measuring the QT interval, and heart rhythm specialists sometimes don't get it right either. And we all know that the 12 lead ECG itself is vexed by its computer algorithms at getting the QTC just right, compared to those of us who would view ourselves as QT aficionados. And so we were hoping that a machine learning approach would solve this and help us glean, one, a very accurate QTC, as accurate as I can make it when I measure it, or core labs that do QT measuring for living. Dr. Michael Ackerman: And two, could we get that QTC from just a couple of leads to be as accurate as what the whole 12 lead ECG would be seeing so that we can move it to a mobile smartphone enabled solution? And so that was our hypothesis going forward, and we studied a lot of patients. And that's something that machine learning and the power of computation does, that in my world, I'm used to studying a hundred or a thousand patients with congenital long QT syndrome and thinking that I've assembled a large cohort, but for this study, we started with over two and a half million ECGs from over 650,000 people. And then ultimately, through training, testing, and validation of about 1.6 million ECGs from over a half a million individuals to sort of teach the computer or have the AI algorithm get the QT interval not too hot, not too cold, but just right. And as we'll discuss, I think we hit the mark. Dr. Greg Hundley: Thanks so much, Mike, what did you find? Dr. Michael Ackerman: Ultimately, we were able to show that with this drill, we could get the deep neural network derived QTC to be give or take two plus minus 20 milliseconds from what would the standard of care, and that being a technician over-read QTC. But then we took, I would say, pretty unique to AI studies, as many AI studies, just do training, testing, and validation for study number one. And then a future paper of a prospective study. But we did that prospective study within this single paper with a subsequent about two year enrollment of nearly 700 patients that I evaluated in our genetic heart rhythm clinic at Mayo Clinic. And half of those patients have congenital long QT syndrome, half did not. And what we showed was that the deep neural network derived QTC from a mobile ECG approximated the subsequent or the just prior 12 lead ECG within one millisecond, +/- 20 millisecond territory. Dr. Michael Ackerman: And it's ability to say is the QTC above or below 500, which we all know is sort of a warning sign, that's a very actionable ECG finding, do something about it, that that 500 millisecond cutoff by the deep neural network gave us an area under the curve of 0.97, which from a screening perspective, that AUC is far higher than a lot of AUCs for a lot of screening tests done in the cancer world and so forth. And so we think we are very close to what I've called a pivot point, where we will soon pivot from the way we've been doing the QTC since Eindhoven over a century ago to a fundamentally new way of deriving a QTC that's precise and accurate and mobile enabled. Dr. Greg Hundley: Very nice, Mike. So using machine learning to accurately assess the QTC from just two leads of an electrocardiogram. Well Chris, you also have a paper in this issue of circulation that pertains to another application of machine learning and looking at the electrocardiogram. Can you describe for us your study population, study design, and then also the question you were trying to address? Dr. Christopher Haggerty: Sure. Yeah, thanks Greg. Great to be here with you all today. Very similar to Mike's study, the motivation for us was we believe very strongly that there's opportunities with using deep learning applied to ECG data to uncover not only new knowledge latent in the ECG itself related to the current patient context, but also to try to predict future outcomes, future events. And that was really our motivation, was to take that paradigm of looking forward, in this case to predict new onset of atrial fibrillation within a year. We used our Geisinger patient cohort, which is a largely rural population in central Pennsylvania. We have very longitudinal data for a lot of our patients, which allows us to have this kind of design going back in our electronic health records, in this case, our ECG database to 30 plus years. Dr. Christopher Haggerty: Similar big numbers that Mike described, and in our case, 1.6 million ECGs over 430,000 patients used to train the model. And we had several different study designs that we employed. One just being a simple proof of concept, asking can we accurately predict new onset atrial fibrillation one year? And then a second study design that was intended to simulate a real world deployment scenario. Obviously the main rationale for trying to predict atrial fibrillation is to then be able to treat and try to prevent stroke. And so we tried to, as best we can in a retrospective fashion, simulate a scenario in which we might use this model to identify patients who went on to have a presumably AFib associated stroke. Dr. Greg Hundley: And what did you find, Chris? Dr. Christopher Haggerty: So I think there are three main findings that we highlighted here. So first, obviously we were building on the great work that Mike and some of his colleagues at the Mayo Clinic have done, showing that looking at AFib using deep neural networks needs to be feasible. We extended it in this case by looking out further than just an acute sense, looking at that one-year outcome. And we had an area under the curve for our proof of concept of 0.85. So area under the curve of 0.85 to identify patients with new onset of atrial fibrillation within one year in our millions of ECGs. Looking at it another way, the second main finding was that that one year prediction was shown to have prognostic significance beyond that one year, which is really interesting and warrants a lot of further study. Looking over 30 years of follow-up, patients predicted to be at high risk at baseline had a hazard ratio of 7.2 for developing atrial fibrillation, compared to those deemed to be low risk. Dr. Christopher Haggerty: And then really the third, and I think perhaps the most exciting finding that we had here, was this simulated stroke experiment that we had, where we identified patients from an internal stroke registry and identified patients who had new diagnosis of AFib at the time or up to a year after the stroke. So we can assume that they were an AFib associated stroke. And subsequently, or I should say previously, had an ECG that we could use to run through the algorithm to predict their atrial fibrillation risk. And we showed that the model performed well in this setting, that of the 375 strokes that we identified, for example, over a five-year period in our registry, we were able to identify 62% of them within three years based on that ECG. So a number needed to screen for an atrial fibrillation associated with stroke about 162, which compares favorably well to other screening techniques that are out there, obviously. So we took that as a great proof of concept that this type of AI technique might have benefits for screening for atrial fibrillation and preventing strokes. Dr. Greg Hundley: Well congratulations, Chris. Well, we're now going to turn to our associate editor, Dr. Nick Mills. And Nick, you have a lot of manuscripts come across your desk. What attracted you to these two papers, and what are the significance of the results as they apply to ECG applications as we move forward? Nick Mills: Thanks, Greg. Yeah, this is a rapidly growing field, where the availability of data scale with digital archiving and lots of really interesting new methodologies are available to our researchers. So we are receiving a lot of content in this area. What I loved about these two papers is not just the quality of the work, but also the really tangible benefits, potentially, for patients. So AI does not need to be complex, but it does need to solve a tangible problem. I guess what we look for in the journal, beyond the kind of innovation and methodology, is quality, and these studies used prospective validation, really reliable end points, ascertainments, transparency, reporting, all the things that we know are important for high quality clinical research. I think the idea that we can bring QT monitoring to the drug store on a portable device for our patients is potentially transformative. I also think that to take a technology, the electrocardiogram that we've been using for over a century, and provide new insights that go way beyond my ability to interpret the ECG, that might help us recommend a different course of action for our patients is also just really exciting. Dr. Greg Hundley: Very nice. Thank you, Nick. Well Mike ... we're going to turn to Mike Rosenberg now, listeners. And Mike wrote a wonderful editorial, and I would invite you to work through this. As you have an opportunity to read the journal and interact with it. Mike, there are two different types of machine learning, I think, that you described were used by the two respective investigative groups. Could you describe those for our cardiology listeners? What were the differences in those two approaches? Dr. Michael Rosenberg: Yeah, sure. And thank you for the opportunity to write the editorial. Two very fascinating papers. I should say that they both use the same approach of what's called supervised learning, where you basically have a set of data inputs, and you're trying to predict a labeled outcome. And what I talk about in the paper is that what we've learned is if you have enough data and enough computing power, you can predict almost anything highly accurately. What's interesting about the two papers, and what I sort of tried to contrast in the editorial, is that the one from the Mayo Group and Dr. Ackerman, was basically predicting what's already a known biomarker for sudden death, which is the QT interval. And essentially, almost trying to automate that process of predicting it accurately and in a way that, in essence, could allow a home monitoring of patients for QT prolongation, which obviously would be a huge benefit for clinicians, all those alerts and things, to be able to have patients taking drugs that are known to prolong the QT interval and feeling comfortable that if they have any prolongation, it could be detected accurately. Dr. Michael Rosenberg: The second one, which is sort of interesting, and in contrast is from the Geisinger Group and Dr. Haggerty, was the approach of ... where actually the prediction itself is actually the biomarker. And we don't actually know exactly what it's using, which I talk about a little bit of what that means and the implications clinically, but in essence, what they showed was that it actually is a very good biomarker and on par with what a lot of us would consider to be very strong predictors of agents. So I think it was two very interesting approaches to, again, applying the same type of machine learning, but really approaching it one from a more discovery side and another from sort of validated or almost automating something that we do on a daily basis. Dr. Greg Hundley: Thank you, Mike. So Mike, just coming back to you again, as we read the literature, and most of us are clinicians or researchers practicing, what should we look for when these new machine learning manuscripts and research studies come out as to gauge, "Ah, this is a really good study," or maybe not so much? Dr. Michael Rosenberg: Yeah. And it's a good question. I think one of the biggest challenges, as I talked about, is interpretability. I think in the clinical world, we're used to understanding the code for the variables that go into our risk prediction model. And so I think first and foremost is can I even understand what this is predicting or am I sort of expected to take the predictions as sort of a black box, it is what it is type of approach? I think that there's other things that I just look at when I'm reviewing these manuscripts. I mean, as I sort of mentioned, what these models are really doing, it's not anything magical. What they're doing is identifying patterns in the data and then using those to make predictions, again, toward whatever label that you've assigned them to. Dr. Michael Rosenberg: It's important that your data sets are split and that you're training at one data set and then testing it in one that's separate. And again, you can't ignore epidemiology. Is the data set that you're training it reflective of the population that you're going to be using those models in? And we know from outside of healthcare, there's issues with models that have been trained in one population where it's potentially biased or it's potentially offering predictions that are using information we may not necessarily want to use. Recidivism is a big example of that. So I think that that's, first and foremost, it's sort of taking a step back as a clinician and saying, "If this was a biomarker that someone was proposing to use to predict some new disease, what would I expect to use to evaluate that?" And that's probably what I would start with. Dr. Greg Hundley: Excellent. Well, I'm going to turn back and go back to our panelists here, listeners. And we're going to ask each of our panelists in about 20 seconds to describe for us what they think is the next most important aspect of research in their respective areas. So first I'll start with Mike Ackerman. Mike, can you tell us what's coming next in this area of assessment of QT prolongation or other aspects of the electrocardiogram? Dr. Michael Ackerman: I think next is implementing this in the real world. We are having our suite of the AI ECG as a  hypertrophic cardiomyopathy detector. We've shown that as an ejection fraction detector, and now as a QT detector in AFib, from our work and Chris's work. And for the QT itself, I think where we are is we're really, really close to now having a mobile enabled digital QT meter. And a digital QT meter, once FDA cleared, then allows the QTC to truly emerge as the next vital sign. And it really deserves to be a vital sign. We use it as a vital sign. We know I want to know my patient's QTC every bit as I want to know his or her weight, blood pressure, saturation. It's an actionable finding, and we're now getting really close. We're just on the cusp of having a true digital QT meter. Dr. Greg Hundley: Excellent. Chris? Dr. Christopher Haggerty: I think for us to, in part address some of the comments that Mike brought up about the reproducibility of these types of models, we're very keen to demonstrate the prospective capabilities of our models to enroll patients in a prospective fashion, run their ECG through our predictor, and then screen them for AFib to determine how well we actually do moving forward, instead of just relying solely on our retrospective data. So we're very excited to do that. We're ramping up for that trial now and hope to be able to demonstrate similarly positive findings from our technique. Dr. Greg Hundley: Great. How about you, Nick? Nick Mills: I'd like to see the same quality and rigor applied to the implementation of these technologies as we have to other important areas in cardiovascular medicine. I think that's a really important step, not just to develop the tools, but to demonstrate their value. But I also think what we've done so far is relatively simplistic. We've taken an ECG and we've ignored almost all the other information that we have in front of us. And as these algorithms are trained and evolved, these and other vital clinical biomarkers and information, and integrating them into these neural networks will really enhance their performance for predicting things that are less tangible, like sudden death in the future or stroke. Dr. Greg Hundley: And then finally, Mike Rosenberg. Dr. Michael Rosenberg: Yeah, I actually see two challenging areas in this field. One is the access to data. And I think one of the things that a lot of companies are realizing is that even if they make hardware, that the data may be more valuable than the technology that they're getting the data from. So I think one is figuring out ways to get access to data so that people can reproduce findings from these studies. And the second is deliverable. A bottle like this is not like the CHADS-VASc score that I can calculate in my head in the clinic. I mean I need a way to actually run these models within an EHR, within a computer system like that. And I think it's going to be a big challenge to take a model like this and to deploy it at scale the way we would with the drug or any other innovation. Dr. Greg Hundley: Fantastic. Well listeners, we want to thank Mike Ackerman from Mayo Clinic, Chris Haggerty from Geisinger, Mike Rosenberg from University of Colorado, and Nick Mills from University of Edinburgh for really providing us with a wonderful discussion regarding the use of machine learning applications in one study to predict the QTC interval from two leads that may be applicable to wearable devices. And in the second study, predicting the future occurrence of atrial fibrillation and even stroke as an adverse event in people at risk. Dr. Greg Hundley: On behalf of both Carolyn and myself, I want to wish you a great week and we will catch you next week on the run. This program is copyright of the American Heart Association, 2021.  

Data Science Course: How to join right data science course? What are various questions and doubts before joining any Data Science Course? Listen to this podcasts on Various questions one need to ask before joining Data Science Course. Kind of mindset to have before joining the program.To enroll Kanth's Career Transition Programs on Data Science/Machine Learning/ Artificial Intelligence https://www.bepec.in/registration-formTo ping Mr Kanth on Instagram @meet_kanth

With the increasing number of sellers joining Amazon, ranking number one is possible only for the fittest. You need to invest the time in new tools and hacks in order survive. In this episode, we have Yinon Shiryan, founder and owner of Quantify Ninja — a 360° set of ninja tools for Amazon Sellers. Yiron's passion for eCommerce and for software development met, grew, and resulted in a full blown integrated software service for professional Amazon sellers. And in this podcast episode, he will share how to step up your Amazon game through AI and machine learning. Special Guest: Yinon Shiryan.

I get way too many questions from aspiring data scientists regarding machine learning. Like what parts of machine learning learning they should learn more about to get a job. And I don't want to disappoint you -- but the thing is that when you get started as a junior, ninety five percent of your projects won't be about Machine Learning. At least, that's a rough average. So what parts of machine learning should you learn more about when preparing for your first job? Well. None? :-) Okay, that's not true. There are some parts that you'll have to know about. I'll talk more about that in this episode. Youtube version: https://youtu.be/yHfRQwOkhJY Original article format here: https://data36.com/machine-learning-algorithms-for-juniors/ LINKS MENTIONED IN THE EPISODE: https://data36.com/linear-regression-in-python-numpy-polyfit/ https://data36.com/jds/ http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ Newsletter: https://data36.com/newsletter Free mini-course: https://data36.com/how-to-become-a-data-scientist/ Check my website: https://data36.com Get access to more data science tutorials, join the inner circle: https://data36.com/inner-circle Find me on Twitter: https://twitter.com/data36_com

Welcome to the next installment of the Anesthesia Patient Safety podcast hosted by Alli Bechtel. This podcast will be an exciting journey towards improved anesthesia patient safety.Today on the show, I discuss the APSF Grant Recipient for 2020, Scott Segal, MD, for his winning project “Development of Machine Learning Algorithms to Predict Difficult Airway Management.” This is an APSF/Medtronic Research Award. Segal’s project seeks to develop a facial recognition machine learning program to replace bedside tests and physical exam findings for difficult airway prediction. Tune in to learn about this project and another exciting study about a difficult airway early warning system.© 2020, The Anesthesia Patient Safety FoundationFor show notes & transcript, visit our episode page at apsf.org: https://www.apsf.org/podcast/12-difficult-airways-and-the-apsf-research-program/

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.13.295592v1?rss=1 Authors: Deng, F., Huang, J., Yuan, X., Cheng, C., Zhang, L. Abstract: Most of the biomedical datasets, including those of omics, population studies and surveys, are rectangular in shape and have few missing data. Recently, their sample sizes have grown significantly. Rigorous analyses on these large datasets demand considerably more efficient and more accurate algorithms. Machine learning (ML) algorithms have been used to classify outcomes in biomedical datasets, including random forests (RF), decision tree (DT), artificial neural networks (ANN) and support vector machine (SVM). However, their performance and efficiency in classifying multi-category outcomes in rectangular data are poorly understood. Therefore, we aimed to compare these metrics among the 4 ML algorithms. As an example, we created a large rectangular dataset using the female breast cancers in the Surveillance, Epidemiology, and End Results-18 (SEER-18) database which were diagnosed in 2004 and followed up until December 2016. The outcome was the 6-category cause of death, namely alive, non-breast cancer, breast cancer, cardiovascular disease, infection and other cause. We included 58 dichotomized features from ~53,000 patients. All analyses were performed using MatLab (version 2018a) and the 10-fold cross validation approach. The accuracy in classifying 6-category cause of death with DT, RF, ANN and SVM was 72.68%, 72.66%, 70.01% and 71.85%, respectively. Based on the information entropy and information gain of feature values, we optimized dimension reduction (i.e. reduce the number of features in models). We found 22 or more features were required to maintain the similar accuracy, while the running time decreased from 440s for 58 features to 90s for 22 features in RF, from 70s to 40s in ANN and from 440s to 80s in SVM. In summary, we here show that RF, DT, ANN and SVM had similar accuracy for classifying multi-category outcomes in this large rectangular dataset. Dimension reduction based on information gain will significantly increase efficiency while maintaining classification accuracy of the models. Copy rights belong to original authors. Visit the link for more info

The new generation of machine-learning decision-making computer algorithms are proliferating - but where is public accountability? And what is 'machine-learning' anyway? Nick explores this brave new world starting with a fiasco in UK education when such an algorithm wrecked student chances of making it to University. Nick will also reveal how he is coding machine-learning algorithms himself.

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.20.260455v1?rss=1 Authors: Handler, D. C. L., Haynes, P. A. Abstract: Assessment of replicate quality is an important process for any shotgun proteomics experiment. One fundamental question in proteomics data analysis is whether any specific replicates in a set of analyses are biasing the downstream comparative quantitation. In this paper, we present an experimental method to address such a concern. PeptideMind uses a series of clustering Machine Learning algorithms to assess outliers when comparing proteomics data from two states with six replicates each. The program is a JVM native application written in the Kotlin language with Python sub-process calls to scikit-learn. By permuting the six data replicates provided into four hundred triplet non redundant pairwise comparisons, PeptideMind determines if any one replicate is biasing the downstream quantitation of the states. In addition, PeptideMind generates useful visual representations of the spread of the significance measures, allowing researchers a rapid, effective way to monitor the quality of those identified proteins found to be differentially expressed between sample states. Copy rights belong to original authors. Visit the link for more info

Don’t know your Supervised from your Unsupervised learning? Do you have your Neural Networks in a not? Fear not, in today's podcast,  Dan and Lee navigate the types of learning that machines can do and how these algorithms work and their application. 

One of the biggest challenges today is that there is so much information out there on a particular topic or person, it is very difficult to ascertain actionable and searchable data. That's where Bill Frishling from FactSquared comes in. From their real-world viral hit, “looking for Trump”, to following congress and intelligent business earnings calls, FactSquared's disruptive search has simplified the aggregated big data industry. Please join us on this episode of the Disruption Everywhere as we explore the current landscape of neural nets, machine-learning, big data, deep fakes and so much more. Podcast show notes available here: https://www.disruptioneverywhere.com/post/giving-meaning-to-chaos-using-neural-nets-and-advanced-machine-learning-algorithms-on-big-data --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/disruptioneverywhere/support

“AI can reduce dramatically the time to finding solutions for COVID-19. But applications that are not supported by domain experts are usually dangerous. They can be based on mistakes or misinterpretations. So it's vital that we understand the scientific methodologists which are being applied.” This is a conversation with Giuseppe Bonaccorso. Giuseppe is Head of Data Science in a large pharmaceutical corporate. His main interests include machine/deep learning, reinforcement learning, big data, and bio-inspired adaptive systems. He is author of several publications including Machine Learning Algorithms and Hands-On Unsupervised Learning with Python, published by Packt Publishing. In this episode, Giuseppe talks about how AI is being applied in healthcare. We talk about whether biases in data could slow down a cure for Covid-19 and the challenges that remain with health data collection, even when it starts with good intentions.

EP 88: Machine Learning, Predictive Analytics, and Other 2020 Marketing Trends In Episode 88 Tyler and Jon discuss Google's push into Facebook's advertising territory with the launch of discovery ads. Jon explains how Google's launch may be easier due to the amount of data they already possess. Tyler continues by sharing Facebook's plan to begin compensating creators on Instagram for content. This could cause some marketing leaders to pull budgets off of secondary platforms like Snapchat or TikTok in order to increase budget with both Facebook and Google.  Afterward, Tyler introduces John Wall, Partner and Head of Business Development at Trust Insights. John is also a host on the Marketing Over Coffee podcast. John explains the importance of analytics at Trust Insights. They use this data, after using a machine learning algorithm, to develop predictive analytics moving forward. John highlights the importance of machine learning in making a comprehensive model of the climate of the future for marketing. He also explains the accessibility to the technology with services like google analytics. With machine learning accessibility, outsourced work can be eliminated depending on which bidding strategies are most optimal. After discussing favorite software and tools along with their integration into machine learning, Tyler and John chat on TikTok’s short form appeal in advertising. Tyler gets into the difficulty on creating sustainable micro content. John reiterates that sustainable content for marketing can be achieved when it can be connected to the customer's journey. Then John gives us his key take away. He shares Trust Insights' motto, urging marketing leaders to utilize their technology to enhance the human experience and tell the story of their brands in a genuine way. After Tyler and John Wall wrap up the interview with an insightful conversation on the economy of podcasting, Our resident wizard, Jon Merlin, delivers our Words of Twisdom. This week they come from @skillongo_com, who shared a quote from Joe Chernov. Both Jon and Tyler highlight the importance of taking the emotional route in connecting with customers. As promised, here's a link to the Marketing Over Coffee Podcast: www.marketingovercoffee.com Looking for a Strategic Marketing Partner to support your brand? Be sure to head over to Fidelitas Development and drop us a line! Timestamps 00:59 - Intro 01:20 - In The News: Googles Launches Discovery Ads 07:58 - Featured Guest | John Wall 08:17 - Trust Insights 09:08 - Most Important PR Metrics 10:10 - Benefits of Machine Learning and AI 13:05 - Predictive Analytics 15:01 - John’s Favorite Tools and Software in Marketing 16:45 - Utilizing Data in Machine Learning Algorithms 18:00 - SEO Keyword Strategies 22:05 - TikTok 26:52 - Marketing Trends 30:33 - Best Take Aways From Marketing Over Coffee 32:39 - Leveraging a Podcast as a Marketing Tool 34:52 - The Marketing Over Coffee Playbook 38:27 - John’s Key Take Away 39:55 - Economics of Podcasting 48:16 - Words of Twisdom 49:00 - Outro   Featured Guests | John Wall LinkedIn What's In the News  Google Launches Discovery Ads Lion’s Share Marketing Podcast Learn More About Tyler & Jon www.tylersickmeyer.com  Need Marketing Help?  www.FidelitasDevelopment.com Music Intro Music – Colony House – Buy “2:20” on iTunes Outro Music – Skillet – Buy “Lions” on iTunes

Let's look into what companies are looking for from Machine Learning.Machine Learning Job Requirement -1: Knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns, identify opportunities, pose business questions, and make valuable discoveries leading to prototype development and product improvement.Machine Learning Job Requirement -2 : • Works to design and develop analytical/ data mining/ machine learning models as part of data science solutions.• Gather, evaluate and document business requirements, translate to data science solution definition, and ability to implement the solution on a Big Data platform.• Ability to design and build an end-to-end prototype data science solution to a business problem in any specific sector/ function. etc..We from BEPEC are ready to help you and make you shift your career at any cost.For more details visit: https://www.bepec.in/Bepec registration form : https://www.bepec.in/registration-formCheck our youtube channel for more videos and please subscribe: https://www.youtube.com/channel/UCn1U...Check our Instagram page: https://instagram.com/bepec_solutions/Check our Facebook Page : https://www.facebook.com/Bepecsolutions/For any help or for any guidance please email enquiry@bepec.in

Episode Notes: Machine learning, artificial intelligence, and data analytics are converging with high-performance computing to advance scientific discovery.

It's hard to think of a single technology that will reshape our world more —in the next 50yrs or so— than Artificial Intelligence. Earlier this year, around April, a new Google AI research centre opened its first-ever African research lab in Accra, Ghana. It plans to host engineers and researchers for AI-related projects. In our 1st episode, find out how Machine Learning Algorithms are slowly changing the way modern society runs and operates. Sources: WIRED - Final Frontiers: Barack Obama on Artificial Intelligence, Autonomous Cars, and The Future of the World. Teachable Machine with Google - Teachable Machine 2.0 makes AI easier for everyone. TEDEd - The Turing Test: Can a computer pass for a human? - Alex Gendler Media: WSJ (The Wall Street Journal), CNA (Channel News Asia: Singapore), Ghana TV, UNESCO, TechPointAfrica, WIRED Magazine Subscribe to our podcast and follow @Benson_Mwaura @vowel_cast

We have a groundbreaking discussion with Prof Philip Hugenholtz co-founder of Microba, a company specialising in Metagenomic Stool Testing using DNA based sequencing to analyse the microbiome. We cover the history and application of DNA based sequencing to classify and identify micro-organisms, using the technology to improve health outcomes on a personalised level, the future prospects for precision medicine, IBD, what a healthy microbiome looks like and much more.     Bio:    Professor Hugenholtz is a microbiologist who has made contributions in the field of culture-independent analysis of microorganisms. He discovered and characterised numerous previously unrecognised major bacterial and archaeal lineages each with greater evolutionary divergence than animals and plants combined. He has participated in the development and application of metagenomics, the genome-based characterisation of microbiomes, which has revolutionised our understanding of microbial ecology and evolution. He has made several discoveries in environmental and clinical microbiology sometimes overturning decades of misdirected culture-based studies.   Topics discussed:   Phil’s Origin story Microbial Classification and Morphology  The Development of DNA based sequencing technology Carl Woese- 16s rRNA - ribosomal RNA sequencing https://en.wikipedia.org/wiki/Carl_Woese Did we evolve from ancient Archaea? Norm Pace- Application of 16s rRNA sequencing in Ecology  https://en.wikipedia.org/wiki/Norman_R._Pace  Culture indépendant analytical techniques Craig Venter- Metagenomic Sequencing  https://en.wikipedia.org/wiki/Craig_Venter Blueprints for identifying bacteria Development of Sequencing Technology Discovery of Microbes Growing Microbes on plates What is the Greengenes reference database 16s rRNA genes identification vs Whole Genome shotgun sequencing (Metagenomics) Metagenomics, Big Data and health patterns The limitation of 16s rRNA technology- conserved genes Whole Genome Sequencing Resolution ITS gene and Fungal Classification Virus and Parasite Classification  The Metagenomic workflow GTDB Database for Whole-genome sequencing Predicting IBD, IBS, Crohns Predicting response to drug response- Depression, Cancer The Gut Microbiome as an early warning system The future of Microbiome What does a healthy cohort’s gut microbiome look like? Discovery of new species Faecalibacterium prausnitzii Anti Inflammatory genes and properties  The uniqueness of the gut microbiome Characterising IBD The impact of the immune system Opportunistic Pathogens- Clostridium difficile, Bilophila wadsworthia, Desulfovibrio, Helicobacter pylori  Gut Metabolite production by microbes- e.g GABA  (a neurotransmitter linked to depression) via KEGG The future of Gut Microbiome Testing Metabolomics Artificial Intelligence and Machine Learning Algorithms for health predictions Diet as a key driver of the microbiome Personalised Diet based on a microbiome profile Prebiotics and Probiotics and Precise Medicine Fibre and Butyrate Balance Phil’s top gut health tip Microbiome changes and improved mental health via diet and exercise      Brought to you by:   Nourishme Organics- The Gut Health Superstore   Check out the Microba Metagenomic Stool Testing and Nutritional Consulting Package for Personalised advice on how to optimise your gut health based on your unique microbiome   https://www.nourishmeorganics.com.au/products/gut-explorer-pro-metagenomic-stool-testing-personalised-nutrition-consultation   Use code guthealthgurus for 10% off     Connect with Prof Phil Hugenholtz   Website- https://www.microba.com/       Connect with Kriben Govender:    Facebook- https://www.facebook.com/kribengee/ Instagram- https://www.instagram.com/kribengovender/ Youtube- https://www.youtube.com/c/Nourishmeorganics?sub_confirmation=1 Gut Health Gurus Facebook Group: https://www.facebook.com/groups/nourishmeorganics/ Deuterium Depletion Support Facebook Group: https://www.facebook.com/groups/347845406055631/   Download links                 If you enjoyed this episode and would like to show your support:   1) Please subscribe on Apple Podcasts, give us 5 stars and leave a positive review     Instructions:   - Click this link  https://itunes.apple.com/au/podcast/gut-health-gurus-podcast/id1433882512?mt=2   - Click "View in Itunes" button on the left-hand side - This will open the Itunes app - Click the "Subscribe" button - Click on "Ratings and Reviews" tab - Click on "Write a Review" button   Non-Itunes users can leave a Google Review here: https://goo.gl/9aNP0V     2) Subscribe, like and leave a positive comment on Youtube   https://www.youtube.com/c/Nourishmeorganics?sub_confirmation=1   3) Share your favourite episode on Facebook, Instagram, and Stories 4) Let your friends and family know about this Podcast by email, text, messenger etc   Thank you so much for your support. It means the world to us.

IT’S HARD TO think of a single technology that will reshape our world more —in the next 50 years—than artificial intelligence. As Yuval Harari says, "It is much harder to struggle against irrelevance than against exploitation." Let's grow #AI literacy.

We have nealry 15+ Machine learning algorithms, but entire machine learning can be summarised into 3 major machine learning equation which rule entire machine learning. Most of the people are learning machine learning courses and planning a career in machine learning. This podcast is pretty important to rule in space of machine learning.We from BEPEC are ready to help you and make you shift your career at any cost.For more details visit: https://www.bepec.in/machinelearningcourseBepec registration form : https://www.bepec.in/registration-formCheck our youtube channel for more videos and please subscribe: https://www.youtube.com/channel/UCn1U...Check our Instagram page: https://instagram.com/bepec_solutions/Check our Facebook Page : https://www.facebook.com/Bepecsolutions/

Most of the data science and machine learning learners are unable to pick right machine learning algorithm for their project. How to pick right machine learning algorithm classification, how to do machine learning algorithm comparison, any impact of deployment on machine learning algorithm for prediction. How to choose between advanced machine learning algorithms and supervised machine learning algorithms. To learn more about above questions refer this podcast. If you are planning to learn data science or machine learning visit www.bepec.in/machinelearningcourseFor more videos on machine learning visit: Our BEPEC Youtube ChannelFor Machine learning Introduction Free Video - Click Here

In season five episode three we chat about take a listener question about Five Papers for Mike Tipping, take a listener question on AIAI and chat with Eoin O'Mahony of Uber Here are Neil's five papers. What are yours? Stochastic variational inference by Hoffman, Wang, Blei and Paisley http://arxiv.org/abs/1206.7051 A way of doing approximate inference for probabilistic models with potentially billions of data ... need I say more? Austerity in MCMC Land: Cutting the Metropolis Hastings by Korattikara, Chen and Welling http://arxiv.org/abs/1304.5299 Oh ... I do need to say more ... because these three are at it as well but from the sampling perspective. Probabilistic models for big data ... an idea so important it needed to be in the list twice.  Practical Bayesian Optimization of Machine Learning Algorithms by Snoek, Larochelle and Adams http://arxiv.org/abs/1206.2944 This paper represents the rise in probabilistic numerics, I could also have chosen papers by Osborne, Hennig or others. There are too many papers out there already. Definitely an exciting area, be it optimisation, integration, differential equations. I chose this paper because it seems to have blown the field open to a wider audience, focussing as it did on deep learning as an application, so it let's me capture both an area of developing interest and an area that hits the national news. Kernel Bayes Rule by Fukumizu, Song, Gretton http://arxiv.org/abs/1009.5736 One of the great things about ML is how we have different (and competing) philosophies operating under the same roof. But because we still talk to each other (and sometimes even listen to each other)  these ideas can merge to create new and interesting things. Kernel Bayes Rule makes the list. http://www.cs.toronto.edu/~hinton/absps/imagenet.pdf An obvious choice, but you don't leave the Beatles off lists of great bands just because they are an obvious choice.

The current gold standard for measuring and tracking brain health is not only hugely expensive, but time-consuming, invasive and cumbersome for the patient, and not as accurate as it could be. Aura Analytics is a health tech company that's providing a new, inexpensive, and highly accurate method for early detection and longitudinal tracking in brain health, which encompasses Alzheimer's disease, Parkinson's disease, ALS, and other disorders affecting fine motor and cognitive function.  CEO and co-founder of Aura Analytics, Daniel Jones, explains how their product works: patients complete a simple, three to five minute set of tasks on a mobile device app that asks specific questions targeting task-specific areas of the brain. The tasks solicit a speech sample from the patient, which is immediately sent to the cloud and subjected to a series of machine learning algorithms that generate and deliver to clinicians in real time objective, finite results. Built on 25 years worth of data from the National Science Foundation and National Institutes of Health, the Aura Analytics technology has shown a level of accuracy in the 90th percentile of all currently used methods of measuring brain health. Patient feedback has already been overwhelmingly positive, and Aura Analytics is now partnering with major pharmaceutical companies and health systems to carry out large-scale clinical trials and gain more widespread acceptance. Daniel Jones expects their first major breakthrough with the FDA to happen in about 18 months, and in the meantime, his efforts are focused on gaining external validation and eventual clinician and consumer adoption. Tune in for the full discussion, and visit auralanalytics.com to learn more.  

The world today generates an immense amount of data. Companies gather data on our buying habits, our location, and how we spend our time. The enormity of this data  is too much for human analysts to dig through alone.  Instead, they use machine learning algorithms. These algorithms take big data to analyze your routines,  infer […] The post Ep 43: Is there Bias in Machine Learning Algorithms? – with guest Dr. Joshua Kroll appeared first on SparkDialog.

Back in late 2017, we did a show about expensify and how the organization was using a service called 'Amazon Mechanical Turk' (MTurk) to process receipts and to help train their Machine Learning Algorithms. You can download that show and listen to it here:  2017-040 #infosec people on Twitter and elsewhere were worried about #privacy issues, as examples of receipts on MTurk included things like business receipts, medical invoices, travel receipts and the like. One of our Slack members (@nxvl) came on our #Slack channel after the show reached out and said that his company uses services like these at their company. They use these services to test applications, unit testing, and creation of test cases for training and refinement of their own applications and algorithms. We discuss the privacy implications of employing these services, how to reduce the chances of data loss, the technology behind how they make the testing work, and what other companies should do if they want to employ the Mturk, or other 3rd parties. Direct Show Download:   http://traffic.libsyn.com/brakeingsecurity/2018-003-MTurk-NXVL-privacy_issues_using_crowdsourced_applications.mp3   ANNOUNCEMENTS: Ms. Amanda Berlin is running 4 session of her workshop "Disrupting the Killchain" starting on the 4th of February at 6:30pm Pacific Time (9:30 Eastern Time)  If you would like to sign up, the fee is $100 and you can send that to our paypal account at https://paypal.me/BDSPodcast  Course Syllabus:   https://docs.google.com/document/d/12glnkY0nxKU9nAvekypL4N910nd-Nd6PPvGdYYJOyR4/edit     If you have an interesting security talk and fancy visiting Amsterdam in the spring, then submit your talk to the Hack In The Box #HITB Amsterdam conference, which will take place between 9 and 13 April 2018. Tickets are already on sale,  And using the checkout code 'brakeingsecurity' discount code gets you a 10% discount". Register at https://conference.hitb.org/hitbsecconf2018ams/register/     #Spotify: https://brakesec.com/spotifyBDS RSS: https://brakesec.com/BrakesecRSS #Youtube Channel:  http://www.youtube.com/c/BDSPodcast #iTunes Store Link: https://brakesec.com/BDSiTunes #Google Play Store: https://brakesec.com/BDS-GooglePlay Our main site:  https://brakesec.com/bdswebsite   Join our #Slack Channel! Email us at bds.podcast@gmail.com or DM us on Twitter @brakesec #iHeartRadio App:  https://brakesec.com/iHeartBrakesec #SoundCloud: https://brakesec.com/SoundcloudBrakesec Comments, Questions, Feedback: bds.podcast@gmail.com Support Brakeing Down Security Podcast by using our #Paypal: https://brakesec.com/PaypalBDS OR our #Patreon https://brakesec.com/BDSPatreon #Twitter: @brakesec @boettcherpwned @bryanbrake @infosystir #Player.FM : https://brakesec.com/BDS-PlayerFM #Stitcher Network: https://brakesec.com/BrakeSecStitcher #TuneIn Radio App: https://brakesec.com/TuneInBrakesec         Show Notes:     Mr. Boettcher gave a talk (discuss) http://DETSec.org  Brakeing Down Incident Response Podcast   Amanda’s class (starts 4 february, $100 for 4 sessions, $50 for early video access)   I need to mention HITB Amsterdam David’s Resume Review -- Bsides Nash Resume Review  SANS SEC504 Mentor course Guest: Nicolas Valcarcel Twitter: @nxvl   Possible News to discuss: https://www.reddit.com/r/sysadmin/comments/7sn23c/oh_security_team_how_i_loathe_you_meltdown/   Mechanical Turk https://www.mturk.com/ Figure Eight (was CrowdFlower) https://www.figure-eight.com   CircleCi 2.0 https://circleci.com/docs/2.0/   TaskRabbit https://www.taskrabbit.com/   Historically:  https://en.wikipedia.org/wiki/The_Turk   Expensify using Amazon Mechanical Turk https://www.theverge.com/2017/11/28/16703962/expensify-receipts-amazon-turk-privacy-controversy   https://www.wired.com/story/not-always-ai-that-sifts-through-sensitive-info-crowdsourced-labor/ FTA: “"I wonder if Expensify SmartScan users know MTurk workers enter their receipts. I’m looking at someone’s Uber receipt with their full name, pick up, and drop off addresses," Rochelle LaPlante, a Mechanical Turk worker who is also a co-administrator of the MTurk Crowd forum, wrote on Twitter.”   https://www.dailydot.com/debug/what-is-amazon-mechanical-turk-tips/ “About those tasks, they’re called HITs, which is short for Human Intelligence Tasks. A single HIT can be paid as low as a penny but may take only a couple seconds to complete. Requesters often list how long a task is supposed to take, along with the nature of the work and the requirements for completing the work.”   “Since mTurk has been around for over a decade, Amazon has created a special class of workers called Masters Qualification. Turkers with masters have usually completed over 1,000 HITs and have high approval ratings.” Kind of like a Yelp for HIT reviewers?   Are companies like expensify aware of the data that could be collected and analyzed by 3rd parties? Is it an acceptable risk?   Privacy questions to ask for companies that employ ML/AI tech? Are they using Mturk or the like for training their algos? Are they using Master level doers for processing?   Nxvl links: Securely Relying on the Crowd (paper Draft): https://github.com/nxvl/crowd-security/blob/master/Securely%20relying%20on%20the%20Crowd.pdf How to Make the Most of Mechanical Turk: https://www.rainforestqa.com/blog/2017-10-12-how-to-make-the-most-of-mechanical-turk/ How We Maintain a Trustworthy Rainforest Tester Network: https://www.rainforestqa.com/blog/2017-08-02-how-we-maintain-a-trustworthy-rainforest-tester-network/ The Pros and Cons of Using Crowdsourced Work: https://www.rainforestqa.com/blog/2017-06-06-the-pros-and-cons-of-using-crowdsourced-work/ How We Train Rainforest Testers: https://www.rainforestqa.com/blog/2016-04-21-how-we-train-rainforest-testers/ AWS re:Invent: Managing Crowdsourced Testing Work with Amazon Mechanical Turk: https://www.rainforestqa.com/blog/2017-01-06-aws-re-invent-crowdsourced-testing-work-with-amazon-mturk/ Virtual Machine Security: The Key Steps We Take to Keep Rainforest VMs Secure: https://www.rainforestqa.com/blog/2017-05-02-virtual-machine-security-the-key-steps-we-take-to-keep-rainforest-vms/

In machine learning, training large models on massive amount of data usually improved results. Our customers report, however, that training such models and deploying them is either operationally prohibitive or outright impossible for them. Amazon AI Algorithms is designed to solve this problem. It is a collection of distributed streaming ML algorithms that scale to any amount of data. They are fast and efficient because they distribute across CPU/GPU machines and share a collective distributed state via a highly-optimized parameter server. They scale to an infinite amount of data because they operate in the streaming model. This means they require only one pass over the data and never increase their resources consumption, allowing training to be paused, resumed, and snapshotted and even for algorithms to consume kinesis streams directly providing an “always on” training mechanism.  They are production ready.  Trained models are automatically containerized and useable in production using Amazon SageMaker hosting. Finally, we provide a convenient SDK which allows scientists to create new algorithms which operate in this model and enjoy all the benefits above. This talk will discuss our design choices and some of the internal working of the system. It will also describe the distributed streaming model and its numerous benefits to machine learning practitioners. We will show how to invoke large scale learning from Amazon SageMaker, or Amazon EMR, and host the solution. Time permits, we will show how to develop a new Algorithm using the SDK.  

This 68th episode of Learning Machines 101 discusses a broad class of unsupervised, supervised, and reinforcement machine learning algorithms which iteratively update their parameter vector by adding a perturbation based upon all of the training data. This process is repeated, making a perturbation of the parameter vector based upon all of the training data until a parameter vector is generated which exhibits improved predictive performance. The magnitude of the perturbation at each learning iteration is called the “stepsize” or “learning rate” and the identity of the perturbation vector is called the “search direction”. Simple mathematical formulas are presented based upon research from the late 1960s by Philip Wolfe and G. Zoutendijk that ensure convergence of the generated sequence of parameter vectors. These formulas may be used as the basis for the design of artificially intelligent smart automatic learning rate selection algorithms. For more information, please visit the official website:  www.learningmachines101.com    

In this episode of the Data Show, I spoke with Ken Stanley, founding member of Uber AI Labs and associate professor at the University of Central Florida. Stanley is an AI researcher and a leading pioneer in the field of neuroevolution—a method for evolving and learning neural networks through evolutionary algorithms. In a recent survey […]

What does it mean to tune an algorithm, how does it matter in a business context, and what are the approaches being developed today when it comes to tuning algorithms? This week's guest helps us answer these questions and more. CEO and Co-Founder Scott Clark of SigOpt takes time to explain the dynamics of tuning, goes into some of the cutting-edge methods for getting tuning done, and shares advice on how businesses using machine learning algorithms can continue to refine and adjust their parameters in order to glean greater results.

This 60th episode of Learning Machines 101 discusses how one can use novelty detection or anomaly detection machine learning algorithms to monitor the performance of other machine learning algorithms deployed in real world environments. The episode is based upon a review of a talk by Chief Data Scientist Ira Cohen of Anodot presented at the 2016 Berlin Buzzwords Data Science Conference. Check out: www.learningmachines101.com to hear the podcast or read a transcription of the podcast!

Commentary by Dr. Valentin Fuster

Anthony Lake is the Principal Data Scientist for Complexica. Anthony has experience in leadership and management, but his current role is very hands-on. But he uses his leadership skills to influence clients and stakeholders within the business to get the best outcomes. Listen now.

In this week's podcast InfoQ’s editor-in-chief Charles Humble talks to Data Scientist Cathy O’Neil. O'Neil is the author of the blog mathbabe.org. She was the former Director of the Lede Program in Data Practices at Columbia University Graduate School of Journalism, Tow Center and was employed as Data Science Consultant at Johnson Research Labs. O'Neil earned a mathematics Ph.D. from Harvard University. Topics discussed include her book “Weapons of Math Destruction,” predictive policing models, the teacher value added model, approaches to auditing algorithms and whether government regulation of the field is needed. Why listen to this podcast: - There is a class of pernicious big data algorithms that are increasingly controlling society but are not open to scrutiny. - Flawed data can result in an algorithm that is, for instance, racist and sexist. For example, the data used in predictive policing models is racist. But people tend to be overly trusting of algorithms because they are mathematical. - Data scientists have to make ethical decisions even if they don’t acknowledge it. Often problems stem from an abdication of responsibility. - Auditing for algorithms is still a very young field with ongoing academic research exploring approaches. - Government regulation of the industry may well be required. Notes and links can be found on http://bit.ly/2eYVb9q Weapons of math destruction 0m:43s - The central thesis of the book is that whilst not all algorithms are bad, there is a class of pernicious big data algorithms that are increasingly controlling society. 1m:32s - The classes of algorithm that O'Neil is concerned about - the weapons of math destruction - have three characteristics: they are widespread and impact on important decisions like whether someone can go to college or get a job, they are somehow secret so that the people who are being targeted don’t know they are being scored or don’t understand how their score is computed; and the third characteristic is they are destructive - they ruin lives. 2m:51s - These characteristics undermine the original intention of the algorithm, which is often trying to solve big society problems with the help of data. More on this: Quick scan our curated show notes on InfoQ. http://bit.ly/2eYVb9q You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. bit.ly/24x3IVq

Thu, 1 Jan 2015 12:00:00 +0100 https://epub.ub.uni-muenchen.de/25580/1/MA_Pritzsche.pdf Pritzsche, Uwe