Machine learning is often fragile in production. For today's Five-Minute Friday episode, Dr. Dan Shiebler details how we can make ML more resilient.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AutoML with Erin LeDell — it rhymes! In today's episode, H2O.ai's Chief ML Scientist guides us through what Automated Machine Learning is and why it's an advantageous technique for data scientists to adopt.
Dr. LeDell:
• Has been working at H2O.ai — the cloud A.I. firm that has raised over $250m in venture capital and is renowned for its open-source AutoML library — for eight years.
• Founded (WiMLDS) Women in Machine Learning & Data Science (100+ chapters worldwide).
• Co-founded R-Ladies Global, a community for genders currently underrepresented amongst R users.
• Is celebrated for her talks at leading A.I. conferences.
• Previously was Principal Data Scientist at two acquired A.I. startups.
• Holds a Ph.D. from the Berkeley focused on ML and computational stats.
Today’s episode is relatively technical so will primarily appeal to technical listeners, but it would also provide context to anyone who’s interested to understand how key aspects of data science work are becoming increasingly automated.
In this episode, Erin details:
• What AutoML — automated machine learning — is and why it’s an advantageous technique for data scientists to adopt.
• How the open-source H2O AutoML platform works.
• What the “No Free Lunch Theorem” is.
• What Admissible Machine Learning is and how it can reduce the biases present in many data science models.
• The new software tools she’s most excited about.
• How data scientists can prepare for the increasingly automated data science field of the future.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Today's guest is mighty Erik Bernhardsson — creator of Spotify's music recommender, prolific open-source developer, world-leading technical blogger, and now model-deployment-tool entrepreneur via Modal Labs.
Erik:
• Is the Founder and CEO of Modal Labs, a startup building innovative tools and infrastructure for data teams.
• Previously was CTO of the real estate startup Better, where he grew the engineering team from the size of 1 — himself — to 300 people.
• Was also previously an Engineering Manager at Spotify, where he created their now-ubiquitous music-recommendation algorithm.
• Is a prolific open-sourcer, having created the popular Luigi and Annoy libraries, among several others.
• Is an industry-leading blogger with posts that frequently feature on the front page of Hacker News.
Today’s episode gets deep into the weeds at points, so it will be particularly appealing to practicing data scientists, ML engineers, and the like, but much of the fascinating, wide-ranging conversation in this episode will appeal to any curious listener.
In this episode, Erik details:
• How the Spotify music recommender he built works so well at scale.
•The litany of new data science and engineering tools he’s excited about and thinks you should be excited about too.
•What open-source library he would develop next.
•Why he founded his Modal and how their tools empower data teams.
• Having interviewed more than 2000 candidates for engineering roles, his top tips both for succeeding as an interviewer and as an interviewee.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In today's remarkable episode, Dr. Kenneth Stanley uses evidence from his machine learning research on Open-Ended A.I. and evolutionary algorithms to inform how you as a human can achieve great life outcomes.
Ken:
• Co-authored the book "Why Greatness Cannot be Planned", a genre-defying book that leverages his ML research to redefine how a human can optimally achieve extraordinary outcomes over the course of their lifetime.
• Was until recently Open-Endedness Team Leader at OpenAI, one of the world’s top A.I. research organizations.
• Led Core A.I. Research for Uber A.I.
• With Prof. Gary Marcus and others, founded A.I. startup Geometric Intelligence, which was acquired by Uber.
• Was Professor of Computer Science at the University of Central Florida.
• Holds a dozen patents for ML innovations, including open-ended and evolutionary (especially neuroevolutionary) approaches.
Today’s episode does get fairly deep into the weeds of ML theory at points so may be best-suited to technical practitioners. That said, the broad strokes of the episode could be not only informative but, again, could indeed be life-perspective-altering for any curious listener.
In this episode, Ken details:
• What genetic ML algos are and how they work effectively in practice.
• How the Objective Paradox — that you fail to achieve an objective you seek — is common across ML and human pursuits.
• How an approach called Novelty Search can lead to superior outcomes than pursuing an explicit objective, again both for machines and humans.
• What Open-Ended A.I. is and its intimate relationship with AGI, a machine with the same learning potential as a human.
• His vision for how A.I. could transform life for humans in the coming decades.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
This week, iconic Stanford University Deep Learning instructor and entrepreneur Kian Katanforoosh details how ML powers his EdTech platform Workera, enabling you to systematically fill gaps in your data science skills.
Kian:
• Is Co-Founder and CEO of Workera, a Bay Area education technology company that has raised $21m in venture capital to upskill workers, with a particular early focus on upskilling technologists like data scientists, software developers, and machine learning specialists.
• Is a lecturer of computer science at Stanford University (specifically, he teaches the extremely popular CS230 Deep Learning course alongside Prof. Andrew Ng, one of the world’s best-known data scientists).
• Was awarded Stanford’s highest teaching award.
• Is also a founding member of DeepLearning.AI, a platform through which he’s taught over three million students deep learning.
• Holds a Masters in Math and Computer Science from CentraleSupélec.
• Holds a Masters in Management Science and Engineering from Stanford.
By and large, today’s episode will appeal to any listener who’s keen to understand the latest in education technology, but there are parts here and there that will specifically appeal to practicing technologists like data scientists and software developers.
In this episode, Kian details:
• What a skills intelligence platform is.
• Four ways that machine learning drives his skills intelligence platform.
• What frameworks and software languages they selected for building their platform and why.
• What he looks for in the data scientists and software engineers he hires.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Analogous to the role DevOps plays for software development, MLOps enables efficient ML training and deployment. MLOps expert Mikiko Bazeley is our guide!
Mikiko:
• Is a Senior Software Engineer responsible for MLOps at Intuit Mailchimp.
• Previously held technical roles at a range of Bay Area startups, with responsibilities including software engineering, MLOps, data engineering, data science, and data analytics.
• Is a prominent content creator on MLOps – across live workshops, her YouTube channel, her personal blog, and the NVIDIA blog.
Today’s episode will appeal primarily to hands-on practitioners such as data scientists and software engineers.
In this episode, Mikiko details:
• What MLOps is.
• Why MLOps is critical for the efficiency of any data science team.
• The three most important MLOps tools.
• The four myths holding people back from MLOps expertise.
• The six most essential MLOps skills for data scientists.
• Her productivity tricks for balancing software engineering, content creation, and her athletic pursuits.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In recent weeks, my YouTube videos have covered Probability concepts like Events, Sample Spaces, and Combinatorics. Today's video features exercises to test and cement your understanding of those concepts.
We will publish a new video from my "Probability for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
Combinatorics is a field of math devoted to counting. In this week's YouTube video, we use examples with real numbers to bring Combinatorics to life and relate it to Probability Theory.
We will publish a new video from my "Probability for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
In this week's YouTube tutorial, we consider probabilistic events where we have multiple independent observations — such as flipping a coin two or more times instead of just once.
We will publish a new video from my "Probability for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
In this week's YouTube tutorial, I introduce the most fundamental atoms of probability theory: events and sample spaces. Enjoy 😀
We will publish a new video from my "Probability for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
This week, we start digging into the actual, uh, theory of Probability Theory. I also highlight the field's relevance to Machine Learning and Statistics. Enjoy 😀
We will publish a new video from my "Probability for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
This week's YouTube video is a quick introduction to the fascinating history of Probability Theory. Next week, we'll actually start digging into Probability Theory, uh, theory 😉
We will publish a new video from my "Probability for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
Last Wednesday, we released the final video of my Calculus course, so today we begin my all-new YouTube course on Probability and Information Theory. This first video is an orientation to the course curriculum, enjoy!
We will publish a new video from my "Probability for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
It's my birthday today! In celebration, I'm delighted to be releasing the final video of my "Calculus for Machine Learning" YouTube course. The first video came out in May and now, ten months later, we're done! 🎂
We published a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday since May 6th, 2021. So happy that it's now complete for you to enjoy. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Probability, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
Starting next Wednesday, we'll begin releasing videos for a new YouTube course of mine: "Probability for Machine Learning". Hope you're excited to get going on it :)
his article was originally adapted from a podcast, which you can check out here.
For last week’s Five-Minute Friday episode, I provided a summary of the various methods of undertaking my deep learning curriculum, be it via YouTube, my book, or the associated repository of GitHub code. I mentioned at the end of the episode that while teaching this deep learning content to students online and in-person, I discovered that many folks could use a primer on the foundational subjects that underlie machine learning in general and deep learning in particular. So after publishing all my deep learning content, I set to work on creating content that covers these subjects that are critical to understanding machine learning expertly — namely, those subjects are linear algebra, calculus, probability, statistics, and computer science.
Way back in Episode #474 of this podcast, I detailed why these particular subject areas form the sturdy foundations of what I call the Machine Learning House . As a quick recap, the idea is that to be an outstanding data scientist or ML engineer, it doesn't suffice to only know how to use machine learning algorithms via the abstract interfaces that the most popular libraries (e.g., scikit-learn, Keras) provide. To train innovative models or deploy them to run performantly in production, an in-depth appreciation of machine learning theory may be helpful — or even essential. To cultivate such an in-depth appreciation of ML, one must possess a working understanding of the foundational subjects, which again are linear algebra, calculus, probability, stats, and computer science:
Read MoreIn this week's tutorial, we use Python code to find the area under the curve of the receiver operating characteristic (the "ROC curve"). This is a machine learning-specific application of integral calculus.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
This is the penultimate video in my Calculus course! After ten months of publishing it, the final video will be released next week :)
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
This article was originally adapted from a podcast, which you can check out here.
Sometimes, during guest interviews, I mention the existence of my deep learning book or my mathematical foundations of machine learning course.
It recently occurred to me, however, that I’ve never taken a step back to detail exactly what content I’ve published over the years and where it’s available if you’re interested in it. So, today I’m dedicating a Five-Minute Friday specifically to detailing what all of my deep learning content is and where you can get it. In next week’s episode, I’ll dig into my math for machine learning content. But, yes, for today, it’s all about deep learning.
Read MoreMy recent videos have covered how to find Definite Integrals manually as well as how to find them computationally using Python code. This week's video is an exercise that tests comprehension of both approaches.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Having detailed how to integrate equations by hand over the past few weeks, this week's video tutorial uses Python code to introduce how to find Definite Integrals computationally — and therefore automatically.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
In recent weeks, my videos introduced Indefinite Integration. Today, we go a step further to calculate *Definite* Integrals. This allows us to find the area under a curve, which is essential for many machine learning models.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.