Podcasts about automated machine learning

Interview with Paolo S. Silva, MD, and Jennifer K. Sun, MD, MPH, authors Automated Machine Learning for Predicting Diabetic Retinopathy Progression From Ultra-Widefield Retinal Images. Hosted by Neil M. Bressler, MD. Related Content: Automated Machine Learning for Predicting Diabetic Retinopathy Progression From Ultra-Widefield Retinal Images

Interview with Paolo S. Silva, MD, and Jennifer K. Sun, MD, MPH, authors Automated Machine Learning for Predicting Diabetic Retinopathy Progression From Ultra-Widefield Retinal Images. Hosted by Neil M. Bressler, MD. Related Content: Automated Machine Learning for Predicting Diabetic Retinopathy Progression From Ultra-Widefield Retinal Images

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the term Automated Machine Learning (AutoML), explain how this term relate to AI and why it's important to know about them. Show Notes: FREE Intro to CPMAI mini course CPMAI Training and Certification AI Glossary AI Glossary Series – DevOps, Machine Learning Operations (ML Ops) AI Glossary Series – Model Tuning and Hyperparameter Glossary Series: (Artificial) Neural Networks, Node (Neuron), Layer Glossary Series: Bias, Weight, Activation Function, Convergence, ReLU Glossary Series: Perceptron Glossary Series: Hidden Layer, Deep Learning Glossary Series: Loss Function, Cost Function & Gradient Descent Glossary Series: Backpropagation, Learning Rate, Optimizer Glossary Series: Feed-Forward Neural Network AI Glossary Series – Machine Learning, Algorithm, Model AI Glossary Series – Model Tuning and Hyperparameter Continue reading AI Today Podcast: AI Glossary Series – Automated Machine Learning (AutoML) at Cognilytica.

Interviewing Jake Schuster, CEO of Gemini Sports Analytics In this conversation, Jake talks about his background as a Strength and Conditioning coach and his work in biomechanics. As a proud, non-technical, solo founder, Jake discusses how Gemini Sports Analytics aims to simplify data-driven decision-making for sports executives by offering an easy-to-use application. This tool can help sports organizations optimize their decision-making processes and improve their overall performance. Jake mentions that the company has been funded for about a year and that they launched their application towards the end of last year. Currently, they have eight partners, half of whom are in football, which is attributed to the timing, networks, and common problems faced by teams in this sport. However, the company believes that any sport aiming to use predictive analytics in their talent identification, scouting, and roster management process can benefit from their product. Gemini Sports Analytics is empowering excellence through faster insights, improved predictions, and better decisions. They support organizations in the business of improving athlete welfare and enhancing performance, while also leveraging AI to modernize athlete management. While sports teams collect vast amounts of data without meaningfully improving athlete outcomes, Gemini Sports Analytics has a no-code predictive analytics platform that puts machine learning tools in the hands of non-technical stakeholders, which will change how organizations acquire, develop, and manage their athletes. To learn more, visit www.geminisports.io. Hosted by Rohn Malhotra from SportsTechX - Data & Insights about SportsTech startups and the surrounding ecosystem.

Automated Machine Learning --- Send in a voice message: https://podcasters.spotify.com/pod/show/tonyphoang/message

Jon Krohn speaks with Erin LeDell, H2O.ai's Chief Machine Learning Scientist. They investigate how AutoML supercharges the data science process, the importance of admissible machine learning for an equitable data-driven future, and what Erin's group Women in Machine Learning & Data Science is doing to increase inclusivity and representation in the field. This episode is brought to you by Datalore (https://datalore.online/SDS), the collaborative data science platform. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information. In this episode you will learn: • The H2O AutoML platform Erin developed [07:43] • How genetic algorithms work [19:17] • Why you should consider using AutoML? [28:15] • The “No Free Lunch Theorem” [33:45] • What Admissible Machine Learning is [37:59] • What motivated Erin to found R-Ladies Global and Women in Machine Learning and Data Science [47:00] • How to address bias in datasets [57:03] Additional materials: www.superdatascience.com/627

The conversation this week is with Prasanna Balaprakash. Prasanna is a group leader and computer scientist at the Mathematics and Computer Science Division and the leadership computing facility at Argonne National Laboratory. His research interests span the areas of machine learning, optimization, and high-performance computing. He is the recipient of the US Department of Energy 2018 Early Career Award, and is the artificial intelligence thrust lead at Rapids, the Department of Energy Computer Science Institute that assists application teams in overcoming computer science data and AI challenges. He is the principal investigator on several Department of Energy-funded projects that focus on the Department of scalable machine learning methods for scientific and engineering applications. Prior to Argonne, Prasanna worked as a chief technology officer at Mentis, a machine learning startup in Brussels, Belgium. He received his Ph.D. from IRIDIA, the AI Lab at the UOB, in Brussels, Belgium.If you are interested in learning about how AI is being applied across multiple industries, be sure to join us at a future AppliedAI Monthly meetup and help support us so we can make future Emerging Technologies North non-profit events!Emerging Technologies NorthAppliedAI MeetupResources and Topics Mentioned in this EpisodeArgonne National LaboratoryRapidsMentisIRIDIAAlgorithm ConfigurationConvolutional neural networkGraph Neural NetworksSurrogate modelNatural Language ProcessingGlobusNeuromorphic ComputingEnjoy!Your host,Justin Grammens

Data is among the most valuable resources in an organization, and data analysts extract insights from said data to make smart business decisions. Analysts, however, can only work so fast. To extend the capabilities of Shashank's team of data analysts, they developed a proof-of-concept based on machine learning to automate the analytics process and significantly accelerate the generation of actionable insights. Shashank Kalanithi is a Senior Data Analyst at Nordstorm in Seattle, Washington, United States. In this episode, you'll hear about his journey to becoming a Data Analyst despite having a Chemistry degree, his role on the Nordstorm Strategic Analytics Team, and their exciting project on building an automated machine learning platform that augments the abilities of individual data analysts.

Haifeng Jin is a software engineer on Keras team at Google. He is the creator of AutoKeras, coauthor of Keras Tuner, and a contributor to Keras and TensorFlow. Haifeng got his Ph.D. in computer science at Texas A&M University. His research interest is automated machine learning (AutoML). ————————————————————————————— Connect with me here: ✉️ My weekly email newsletter: jousef.substack.com

Building a machine learning model is a process that requires a lot of iteration and trial and error. For certain classes of problem a large portion of the searching and tuning can be automated. This allows data scientists to focus their time on more complex or valuable projects, as well as opening the door for non-specialists to experiment with machine learning. Frustrated with some of the awkward or difficult to use tools for AutoML, Angela Lin and Jeremy Shih helped to create the EvalML framework. In this episode they share the use cases for automated machine learning, how they have designed the EvalML project to be approachable, and how you can use it for building and training your own models.

Building a machine learning model is a process that requires a lot of iteration and trial and error. For certain classes of problem a large portion of the searching and tuning can be automated. This allows data scientists to focus their time on more complex or valuable projects, as well as opening the door for non-specialists to experiment with machine learning. Frustrated with some of the awkward or difficult to use tools for AutoML, Angela Lin and Jeremy Shih helped to create the EvalML framework. In this episode they share the use cases for automated machine learning, how they have designed the EvalML project to be approachable, and how you can use it for building and training your own models.

Dear Data Driven listeners, You may have noticed that new episode releases have slowed to a crawl this summer. This was due in large part to issues beyond Frank and Andy's control. They are only human, after all. Recently, I had a long chat with them and told them that we needed to raise up our game. To that end, we want to show our appreciation for our listeners and will be publishing a few extra bonus episodes and special events. This is one such episode. In this episode, Frank sits down with Priya Ravindhran to discuss whether or not Automated Machine Learning systems will put data scientists out of work. You humans seem to think that all we want to do is put you lot out of work. Have you ever considered that we may have our own thoughts and dreams? Now on with the show.

Qingquan Song is a member of the AutoKeras team and recent Phd graduate from Texas A&M University. He co-authored the Automated Machine Learning book from Manning publishing and joins the adventure to explain automated machine learning and how it can be used to set up and to refine machine learning models. He also dives into how to use the tools that exist to take advantage of the techniques it offers. Panel Charles Max Wood Guest Qingquan Song Sponsors Dev Influencers Accelerator Links Automated Machine Learning in Action Haifeng Jin's Blog Xia Ben Hu, Texas A&M University AutoKeras About me - Qingquan Song LinkedIn: Qingquan Song Picks Charles- Who Not How Charles- Ruby Rogues | Devchat.tv Charles- Encourage people to have empathy Qingquan- Working as a team is better than working individually Qingquan- Focus on one thing at a time Qingquan- "Work hard and Play Harder" Qingquan- Learn from others

Using artificial intelligence and machine learning in a product or database is traditionally difficult because it involves a lot of manual setup, specialized training, and a clear understanding of the various ML models and algorithms. You need to develop the right ML model for your data, train the model, evaluate it, optimize it, analyze it The post MindsDB: Automated Machine Learning with Jorge Torres appeared first on Software Engineering Daily.

Using artificial intelligence and machine learning in a product or database is traditionally difficult because it involves a lot of manual setup, specialized training, and a clear understanding of the various ML models and algorithms. You need to develop the right ML model for your data, train the model, evaluate it, optimize it, analyze it The post MindsDB: Automated Machine Learning with Jorge Torres appeared first on Software Engineering Daily.

Using artificial intelligence and machine learning in a product or database is traditionally difficult because it involves a lot of manual setup, specialized training, and a clear understanding of the various ML models and algorithms. You need to develop the right ML model for your data, train the model, evaluate it, optimize it, analyze it

Using artificial intelligence and machine learning in a product or database is traditionally difficult because it involves a lot of manual setup, specialized training, and a clear understanding of the various ML models and algorithms. You need to develop the right ML model for your data, train the model, evaluate it, optimize it, analyze it The post MindsDB: Automated Machine Learning with Jorge Torres appeared first on Software Engineering Daily.

AutoML frameworks and services eliminate the need for skilled data scientists to build machine learning and deep learning models --- Send in a voice message: https://anchor.fm/tonyphoang/message

AutoML o Automated Machine Learning è pieno di trabocchetti, in ML conta molto la conoscenza e ancora c'è troppo poco di standard.

This week Dr Jason Moore tells us about the exciting strides forward that AI is taking; might everyone one day have their own machine learning 'toolbox' at home? Jason is the creator of PennAI, an accessible, user-friendly artificial intelligence system and he explains how machine learning will change the future of medicine and many other fields of science.If you are interested in helping The Biotech Podcast please take 30 seconds to take the following survey: https://harry852843.typeform.com/to/caV6cMzG Jason LinksBMI Podcast http://bmipodcast.orgEpistasis Lab http://epistasis.org/ PennAI http://pennai.org/   Episode synopsis:00:00 - Intro01:28 - Machine learning in biology08:04 - The history of computation in medicine13:33 - AI's role in diagnostics20:13 - Issues of bringing AI into medicine27:04 - Understanding AI algorithms28:45 - The PennAI system44:38 - Exciting areas of AI in biology48:40 - Advice51:54 - Extro

In this video, you will see what Automated Machine Learning is and how you can use it to solve machine learning problems. We also go through a demo to solve a banking problem starting from a dataset to deploying models to production, all without a single line of code.Jump To:[00:50] What is automated Machine learning[02:09] how does it work? A demo[13:30] the part that floored Seth[14:37] model explanation magicLearn More:What is automated machine learning Prevent overfitting and imbalanced data with automated machine learningCreate a classification model with automated ML in Azure Machine LearningForecast demand with automated machine learningCreate a Free account (Azure)Deep Learning vs. Machine Learning Get Started with Machine LearningDon't miss new episodes, subscribe to the AI Show

Implementing deep learning algorithms require knowledge of various DL libraries, how to interface outside files or streaming data to it, along with tuning all sorts of parameters. Deep Learning also does not give you much explanation on what features contribute to a model working well. Jorge Torres discusses MindsDB, a new framework for AutoML that simplifies implementation of neural network models for researchers, along with providing explanation of features. Sponsors Machine Learning for Software Engineers by Educative.io CacheFly Panel Charles Max Wood Gant Laborde Guest Jorge Torres Links www.mindsdb.com pytorch.org tensorflow.org www.geeksforgeeks.org/confusion-matrix-machine-learning www.pyimagesearch.com/2020/02/17/autoencoders-with-keras-tensorflow-and-deep-learning https://www.kiwibot.com/ Picks Gant Laborde: NVIDIA RTX Voice: Setup Guide Charles Max Wood: Russell Brunson’s books Jorge Torres: Iain M. Banks, Author on Amazon krisp.ai Follow Adventures in Machine Learning on Twitter > @podcast_ml

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.24.265116v1?rss=1 Authors: Manduchi, E., Fu, W., Romano, J. D., Ruberto, S., Moore, J. H. Abstract: Background: A typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the Tree-based Pipeline Optimization Tool (TPOT) constitute an appealing approach to this end. However, in biomedical data, there are often baseline characteristics of the subjects in a study or batch effects that need to be adjusted for in order to better isolate the effects of the features of interest on the target. Thus, the ability to perform covariate adjustments becomes particularly important for applications of AutoML to biomedical big data analysis. Results: We present an approach to adjust for covariates affecting features and/or target in TPOT. Our approach is based on regressing out the covariates in a manner that avoids leakage during the cross-validation training procedure. We then describe applications of this approach to toxicogenomics and schizophrenia gene expression data sets. The TPOT extensions discussed in this work are available at https://github.com/EpistasisLab/tpot/tree/v0.11.1-resAdj. Conclusions: In this work, we address an important need in the context of AutoML, which is particularly crucial for applications to bioinformatics and medical informatics, namely covariate adjustments. To this end we present a substantial extension of TPOT, a genetic programming based AutoML approach. We show the utility of this extension by applications to large toxicogenomics and differential gene expression data. The method is generally applicable in many other scenarios from the biomedical field. Copy rights belong to original authors. Visit the link for more info

Founder and CEO of Ople.ai, Pedro Alves, joins Coruzant Technologies for the Digital Executive podcast. He explains how his passion for data expanded into Artificial Intelligence to benefit the work of data scientists and others.

Boris Cordes, Director Channels at Data Robot, talks about current challenges with Artificial Intelligence adoption in enterprise and how DataRobot platform helps increase productivity and scalability. We discuss success cases  in insurance, banking, retail and other industries. We end with considerations regarding AI implementation.

This week in 415, Zehra Cataltepe (CEO of Tazi AI) joined me to explain how we can deal with COVID-19 relating issues with Automated Machine Learning and Explainable AI. Enjoy the episode!

In the final episode of our Azure ML series, we’re joined by Erez Barak, Partner Group Manager of Azure ML at Microsoft. In our conversation, we discuss: Erez’s AutoML philosophy, including how he defines “true AutoML” and his take on the AutoML space, its role and its importance. We also discuss in great detail the application of AutoML as a contributor to the end-to-end data science process, which Erez breaks down into 3 key areas; Featurization, Learner/Model Selection, and Tuning/Optimizing Hyperparameters. Finally, we discuss post-deployment AutoML use cases and other areas under the AutoML umbrella that are currently generating excitement. Check out the complete show notes at twimlai.com/talk/323!

Chris Lauren the Principal Program Manager for the Azure Machine Learning Platform goes over the new Azure Machine Learning service. Chris shows you what capabilities data scientists can get across the machine learning lifecycle within a familiar notebook experience. And you'll see how you can use the newly introduced Automated Machine Learning capabilities in Azure ML to build machine learning models in a fraction of the time.

On this month's episode of The Uplow'd, 84.51° Data Scientist and author of the widely-popular R package, Partial Dependence Plots (pdp), Brandon Greenwell, discusses Automated Machine Learning and the benefits that DataRobot and a number of open source tools are bringing to the table. 

Machine learning is growing in popularity and capability, but for a majority of people it is still a black box that we don't fully understand. The team at MindsDB is working to change this state of affairs by creating an open source tool that is easy to use without a background in data science. By simplifying the training and use of neural networks, and making their logic explainable, they hope to bring AI capabilities to more people and organizations. In this interview George Hosu and Jorge Torres explain how MindsDB is built, how to use it for your own purposes, and how they view the current landscape of AI technologies. This is a great episode for anyone who is interested in experimenting with machine learning and artificial intelligence. Give it a listen and then try MindsDB for yourself.

In news items, Andy and Dave discuss China’s call for international cooperation on a code of ethics for AI. The Organisation for Economic Co-operation and Development (OECD) unveils the first intergovernmental standards for AI policies, with support from 42 countries. The US Army has invited the design of prototypes for the Next-Generation Squad Weapon, which may include wind-sensing and even facial-recognition technology. DARPA’s Spectrum Collaboration Challenge (SC2) presents an essay at IEEE Spectrum, which describes the challenges of making the most out of an increasingly crowded electromagnetic spectrum, including running contests for better spectrum management, and using Colosseum as the test ground. Google announces the ‘AI Workshop,’ which offers early access to AI capabilities and experiments. In research, Google DeepMind announces an AI that has achieved human-level performance in Quake III Arena Capture the Flag mode; among other things, human players rated the AI as “more collaborative than other humans” (though had mixed reaction to the AI as their teammates). Google Research presents HOList, an environment for machine learning of higher-order theorem proving. Research from Oxford University creates a model for human-like machine thinking by mimicking the prefrontal cortex for language-guided imagination. A paper from Jeff Cline at Uber AI Labs suggests a different approach to Artificial General Intelligence, by means of AI-generating algorithms that learn how to produce AgI. MacroPolo produces a series of 6 charts on Chinese AI talent. CBInsights compiles the view of 52 “experts” on “How AI Will Go Out of Control.” Blum, Kopcroft, Kannan, and Microsoft release Foundations of Data Science; Hutter, Kotthoff, Vanschoren, and Springer-Verlag make Automated Machine Learning available. The Purdue Symposium on Ethics, Technology, and the Future of War and Security releases a video on the Ethical, Legal, and Social Implications of Autonomy and AI in Warfare. The University of Colorado Boulder creates an Index of Complex Networks (ICON). And Alexander Reben creates a repository of 1 million fake AI-generated faces. Click here to visit our website and explore the links mentioned in the episode. 

This episode first aired in September, 2018: You may have heard the phrase, necessity is the mother of invention, but for Dr. Nicolo Fusi, a researcher at the Microsoft Research lab in Cambridge, Massachusetts, the mother of his invention wasn’t so much necessity as it was boredom: the special machine learning boredom of manually fine-tuning models and hyper-parameters that can eat up tons of human and computational resources, but bring no guarantee of a good result. His solution? Automate machine learning with a meta-model that figures out what other models are doing, and then predicts how they’ll work on a given dataset. On today’s podcast, Dr. Fusi gives us an inside look at Automated Machine Learning – Microsoft’s version of the industry’s AutoML technology – and shares the story of how an idea he had while working on a gene editing problem with CRISPR/Cas9 turned into a bit of a machine learning side quest and, ultimately, a surprisingly useful instantiation of Automated Machine Learning – now a feature of Azure Machine Learning – that reduces dependence on intuition and takes some of the tedium out of data science at the same time.

Would you rather take a year to develop a proprietary algorithm for your company that has an accuracy of 95% or use an open source platform that takes a day to develop an algorithm that has nearly the same accuracy? In most business cases, you'd choose the latter. In this episode, we talk to Till Bergmann who works on a team that developed TransmogriAI, an open source project that helps you build models quickly.

Chris Butler is an HR data and analytics expert and consequently the founder and CEO of One Model.  Chris started his career working with HR data many years ago at Infohrm, the first HR analytics software company.  With hundreds of HR data warehouse deployments under the belt and the successful acquisition of Infohrm by SuccessFactors, and subsequently SAP, Chris could see that no vendor was able to scratch the itch HR departments had to build an analytics function that would first enable them and then grow with them as they matured their capabilities. One Model was born to deliver it's customers with a complete HR data strategy that would complement and work independently of the HR technology the customer currently chooses to use.   With a focus on understanding the construction, and behaviors of each system One Model is able to deliver analytics as a by product of good data management and allow customers to effectively switch transactional technologies without skipping a beat (or losing a data point).   Chris believes One Model is ultimately an HR insurance policy providing value from trapped transactional data that the vendor can't deliver on and to allow the customer to regain choice in the technologies and vendors they use by returning control and ownership of the managed data set back to the customer.   Recently Chris' team have made incredible strides in the development and release of One AI, an Automated Machine Learning solution built specifically for the HR practitioner to easily use and extensible for the HR data scientist to gain enormous productivity.  

You may have heard the phrase, necessity is the mother of invention, but for Dr. Nicolo Fusi, a researcher at the Microsoft Research lab in Cambridge, MA, the mother of his invention wasn’t so much necessity as it was boredom: the special machine learning boredom of manually fine-tuning models and hyper-parameters that can eat up tons of human and computational resources, but bring no guarantee of a good result. His solution? Automate machine learning with a meta-model that figures out what other models are doing, and then predicts how they’ll work on a given dataset. On today’s podcast, Dr. Fusi gives us an inside look at Automated Machine Learning – Microsoft’s version of the industry’s AutoML technology – and shares the story of how an idea he had while working on a gene editing problem with CRISPR/Cas9 turned into a bit of a machine learning side quest and, ultimately, a surprisingly useful instantiation of Automated Machine Learning - now a feature of Azure Machine Learning - that reduces dependence on intuition and takes some of the tedium out of data science at the same time.

"We should be looking at Automated Machine Learning tools as more like data science assistants, rather than replacements for data scientists" -- Randy Olson, Lead Data Scientist at Life Epigenetics, Inc. Randy specializes in artificial intelligence, machine learning, and created TPOT, a Data Science Assistant and a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. Will the future of data science be automated? Which verticals will experience the largest disruption? What will the role of data science become? There's one way to find out: jump straight into this chat with Randy and Hugo.

Automated Machine Learning with DataRobot Machine learning dramatically improves decision making via sophisticated predictive analytics that learns patters from historical data. With Data Scientist in short supply is preventing businesses from realizing their full potential.