Optimizers in Machine Learning, Featuring Maciej Balawejder - ML 077
Ben and Michael interview Maciej Balawejder, a mechanical engineering student passionate about AI, ML, and robotics. As an active contributor on Medium.com, Maciej has already made significant contributions to the AI and ML communities. On the show, they discuss Maciej’s recent article about optimizers in Machine Learning, plus their personal philosophies and approaches to deep learning.SponsorsTop End DevsCoaching | Top End DevsLinksMaciej Balawejder - MediumOptimizers in Machine LearningAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacyBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
23 Kesä 202248min
Part 2: Exploratory Data Analysis (EDA) Next Steps - ML 076
After ensuring your data has surpassed the hyper parameter tuning phase, what is the next step in your EDA protocol? Today on the show, Ben and Michael continue the discussion on EDA methodology within Machine Learning and discuss linear regression with OLS, decision trees, and common visualization tools for data scientists. In this episode...Linear regression with OLSAccuracy metricsDecision treesShapley valuesCommon visualization toolsSponsorsTop End DevsCoaching | Top End DevsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacyBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
16 Kesä 202250min
Exploratory Data Analysis (EDA) in Machine Learning - ML 075
EDA is primarily used in machine learning to see what data can reveal beyond the formal modeling or hypothesis testing task and provides a better understanding of data set variables and the relationships between them. It can also help determine if the statistical techniques you are considering for data analysis are appropriate. Today on the show, Ben and Michael discuss how to use EDA in machine learning models. In this episode...What is EDA?Tips and Tricks and steps for EDAHow to approach downsamplingUnderstanding feature sets relative to your labelsOptimizing modelsMotivating yourself to get into the dataTools for EDAA few scenarios for discussionWhat is the most detrimental EDA mistake for MLSponsorsTop End DevsCoaching | Top End DevsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacyBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
9 Kesä 202249min
Apache Spark (Pt. 2): MLlib - ML 074
MLlib is Apache Spark's scalable machine learning library. Today, Ben and Michael discuss the ease of use, performance, algorithms, and utilities included in this library and how to execute the best ML workflow with MLlib. In this episode...Why stick with Spark libraries vs. a single node operation?What algorithms are not in Spark Lib?What is the min. package set to use for supervised learning?Modeling and validationDown-sampling your dataMLlib vs. scikit-learnResourcesSponsorsTop End DevsCoaching | Top End DevsLinksMLlib | Apache SparkWhat is PySpark? | Domino Data Science DictionaryUCI Machine Learning RepositoryAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacyBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
2 Kesä 202252min
Apache Spark Integration and Platform Execution for ML - ML 073
Apache Spark is a lightning-fast unified analytics engine for large-scale data processing and machine learning. In this episode, Ben and Michael unpack Spark by ping-ponging questions and answers, supplemented by various examples applicable to machine learning workflows. In this Episode… How does Spark work?What makes Apache Spark effective?Dot repartition in SparkParallel processing systemsWhat is an aggregation in Spark sequel?Analytics with Spark What is MPP?Testing for productionSpark algorithms Sponsors Top End DevsCoaching | Top End DevsSponsored By:Coaching | Top End Devs: Do you want to level up your career? or go freelance? or start a podcast or youtube channel? Let Charles Max Wood Help You Achieve Your DreamsTop End Devs: Learn to Become a Top 5% Developer. Join our community of ambitious and engaged programmers to learn how.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacyBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
26 Touko 202236min
Two Case Studies: Production ML infrastructure and Recommendation Engines - ML 072
Ben and Michael walk through two different cases studies relative to production ML infrastructure and recommendation engines. The first is about a free on-line tutoring service for underserved communities called “Learn to Be”, and the second centers around the online course provider “Coursera”. Ben and Michael set up the case studies with fundamental problem statements, followed by their various approaches to executing the objectives to achieve the desired process outcomes. Sponsors Top End DevsCoaching | Top End DevsSponsored By:Coaching | Top End Devs: Do you want to level up your career? or go freelance? or start a podcast or youtube channel? Let Charles Max Wood Help You Achieve Your DreamsTop End Devs: Learn to Become a Top 5% Developer. Join our community of ambitious and engaged programmers to learn how.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacyBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
18 Touko 202253min
Using AI and ML to Help Humans, Not Replace Them - ML 071
Ben interviews Michael Griffiths, Director of Data Science at ASAPP, a company leveraging AI and ML to augment and automate human work, improve operational efficiencies and customer experiences, and ultimately empower people to be their best. Michael shares specific examples of how this can be done for human agent productivity within contact centers. They also discuss fully human controlled vs automated systems, delivering value with AI and ML, and the future of AI driven technology. In this Episode… How do you deliver value with AI and ML?Fully human controlled vs. fully automated systemsML software engineering vs traditional software Using static training data models and data validationCommunicating process improvements and failures What is the future of ASAPP and AI driven technology? Sponsors Top End DevsCoaching | Top End Devs Links How a Level System can Help Forecast AI Costs - KDnuggetsAI Research - ASAPPASAPPSpecial Guest: Michael Griffiths.Sponsored By:Coaching | Top End Devs: Do you want to level up your career? or go freelance? or start a podcast or youtube channel? Let Charles Max Wood Help You Achieve Your DreamsTop End Devs: Learn to Become a Top 5% Developer. Join our community of ambitious and engaged programmers to learn how.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacyBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
12 Touko 202243min
AutoML Discovery and Approach - ML 070
AutoML (automated machine learning) has become a hot topic over the past few years. Abid Ali Awan joins the show to share his approach to AutoML, when and how to utilize it compared to classic approaches. Ben and Abid also discuss open-source vs. proprietary platforms. What is AutoML? Automated machine learning provides methods and processes to make machine learning available for non-machine learning experts, to improve efficiency of machine learning and to accelerate research on machine learning. 2 levels of implementation: Blackbox AutoML can do one, or all of the things for feature selection with a statistical outset and self optimizing outcome. Whitebox AutoML exposes the code to explain how it behaves and allows you to produce predictions as to the influencing variables. Leveraging open-source toolkits vs. proprietary “The output is the model, the input is the data, and you can use that model to predict anything according to your business problem.” - Abid Ali Awan Sponsors Top End DevsCoaching | Top End Devs Links H2O.aiSpecial Guest: Abid Ali Awan .Sponsored By:Coaching | Top End Devs: Do you want to level up your career? or go freelance? or start a podcast or youtube channel? Let Charles Max Wood Help You Achieve Your DreamsTop End Devs: Learn to Become a Top 5% Developer. Join our community of ambitious and engaged programmers to learn how.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacyBecome a supporter of this podcast: https://www.spreaker.com/podcast/adventures-in-machine-learning--6102041/support.
4 Touko 202243min
