Daily Habit #5: Meditate

Added on February 14, 2022 by Jon Krohn.

This article was originally adapted from a podcast, which you can check out here.

At the beginning of the new year, in Episode #538, I introduced the practice of habit tracking and provided you with a template habit-tracking spreadsheet. Since then, Five-Minute Fridays have largely revolved around daily habits and that theme continues today with my daily habit of meditation.

If you’ve been listening to SuperDataScience episodes for more than a year, you’ll be familiar with my meditation practice already, as I detailed it back in Episodes #434 and 436 — episodes on what I called “attention-sharpening tools”. You can refer back to those episodes to hear all the specifics, but the main idea is that every single day — for thousands of consecutive days now — I go through a guided meditation session using the popular Headspace application.

Indefinite Integral Exercises

Added on February 14, 2022 by Jon Krohn.

If you watched last week's video on the integral calculus rules (or if you already feel confident about them!), you can use this week's video to test your comprehension of the topic.

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.

How Genes Influence Behavior — with Prof. Jonathan Flint

Added on February 14, 2022 by Jon Krohn.

How do genes influence behavior? This week's guest, Prof. Jonathan Flint, fills us in, with a particular focus on how machine learning is uncovering connections between genetics and psychiatric disorders like depression.

In this episode, Prof. Flint details:
• How we know that genetics plays a role in complex human behaviors incl. psychiatric disorders like anxiety, depression, and schizophrenia.
• How data science and ML play a prominent role in modern genetics research and how that role will only increase in years to come.
• The open-source software libraries that he uses for data modeling.
• What it's like day-to-day for a world-class medical sciences researcher.
• A single question you can ask to prevent someone committing suicide.
• How the future of psychiatric treatments is likely to be shaped by massive-scale genetic sequencing and everyday consumer technologies.

Jonathan:
• Is Professor-in-Residence at the University of California, Los Angeles, specializing in Neuroscience and Genetics.
• Leads a gigantic half-billion dollar project to sequence the genomes of hundreds of thousands of people around the world in order to better understand the genetics of depression.
• Originally trained as a psychiatrist, he established himself as a pioneer in the genetics of behavior during a thirty-year stint as a medical sciences researcher at the University of Oxford.
• Has authored over 500 peer-reviewed journal articles and his papers have been cited an absurd 50,000 times.
• Wrote a university-level textbook called "How Genes Influence Behavior", which is now in its second edition.

Today’s episode mentions a few technical data science details here and there but the episode will largely be of interest to anyone who’s keen to understand how your genes influence your behavior, whether you happen to have a data science background or not.

Thanks to Mohamad, Hank, and Serg for excellent audience questions!

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Daily Habit #4: Alternate-Nostril Breathing

Added on February 7, 2022 by Jon Krohn.

This article was originally adapted from a podcast, which you can check out here.

Back in Episode #538, I kicked off the new year of Five-Minute Fridays by introducing the practice of habit-tracking, including providing you with a template habit-tracking spreadsheet. I followed that up in Episodes #540 and 544 by detailing for you my habits of starting the day with a glass of water and making my bed, respectively.

Continuing on with my morning habits, today’s episode is about alternate-nostril breathing (ANB).

ANB is often associated with yoga classes so if you do a lot of yoga, you may have encountered this technique before. However, there’s no reason why you can’t duck into a quick ANB session for a couple of minutes at any time. I like having it as one of my morning rituals because it makes me feel centered, focused, and present; as a result, I find myself both enjoying being alive and ready to tackle whatever’s going to come at me through the day. That said, if I’m feeling particularly stressed out or out of touch with the present moment, I might quickly squeeze in a few rounds of ANB at any time of day.

The Integral Calculus Rules — Topic 86 of Machine Learning Foundations

Added on February 7, 2022 by Jon Krohn.

Integral Calculus Rocks! I mean, Integral Calculus Rules! I mean, this video covers the Integral Calculus Rules 😉... namely, the Power Rule, the Constant-Multiple Rule, and the Sum Rule.

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.

Scaling Data-Intensive Real-Time Applications — with Matthew Russell

Added on February 7, 2022 by Jon Krohn.

This week's guest is indefatigable Matthew Russell. An Air Force veteran and author of four data science books, Matthew is now Founder/CEO of Strongest AI, a leading tech platform for fitness.

In this episode, Matthew covers:
• The tech stack he uses to make it possible to provide data from fitness competitions to millions of spectators all over the world in real-time.
• How he rapidly tests machine learning models for deployment into portable devices like the iPhone and the Apple Watch.
• Multi-objective ML functions and why they’re so widely useful in real-world applications.
• The three critical traits he looks for in anyone he hires.
• The values instilled in him by pursuing a military education.
• The key skills he wishes he’d learned earlier in his career.

A bit more on Matthew... he's:
• Founder and CEO of Strongest, the leading technology platform for global fitness events, which is growing into an application that uses ML models to make you stronger, faster, and fitter than ever before.
• Author of four books published by O'Reilly Media, including the classic "Mining the Social Web", which is now in its third edition.
• Prior to founding Strongest, served as CTO at several firms.
• Holds a BS in Computer Science from the US Air Force Academy as well as an MS in Computer Science and Machine Learning from the US Air Force Institute of Technology.

Parts of today’s episode, particularly in the first half, do get fairly technical as we dig into the open-source software stack that enables the scalable deployment of data-intensive real-time applications. That said, much of the episode will appeal to anyone who’s excited about physical fitness or commercializing A.I.

Shout out to Austin Ogilvie for introducing me to Matthew 😀

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Daily Habit #3: Make Your Bed

Added on January 31, 2022 by Jon Krohn.

Back in Episode #538, I kicked off the new year of Five-Minute Fridays by introducing the practice of habit-tracking, including providing you with a template habit-tracking spreadsheet. I followed that up in Episode #540 by detailing for you my habit of starting the day with a glass of water.

So Daily Habit #1 was the meta-habit of tracking your habits. Daily Habit #2 is the morning water glass. That brings us today to my Daily Habit #3, which is another morning habit: making your bed.

What Integral Calculus Is

Added on January 31, 2022 by Jon Krohn.

What is Integral Calculus and why is it essential to Machine Learning? This week's video answers those questions while also explaining how integral calculus works at a high level and detailing its characteristic notation.

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.

Sparking A.I. Innovation — with Nicole Büttner

Added on January 31, 2022 by Jon Krohn.

Looking for ideas on how to spark A.I. innovation in your organization? Nicole Büttner, the eloquent and effervescent Founder/CEO of Merantix Labs, has concrete A.I. innovation frameworks for you in this week's guest episode.

Merantix Labs is a renowned Berlin-based consultancy that enables companies to unlock the value of A.I. across all industries.

In addition to being Founder and CEO of Merantix Labs, Nicole:
• Is a member of the Management Board of Merantix Labs’ parent company Merantix, an A.I. Venture Studio that has raised $30m in funding from the likes of SoftBank Group Corp. to serially originate successful ML start-ups.
• Holds a Masters in Quantitative Economics and Finance from the University of St.Gallen, the world’s leading German-language business school.
• Was a visiting researcher in Economics at Stanford University.

In this episode, Nicole details:
• What an A.I. Venture Studio is and how she founded a thriving A.I. consultancy within it
• How to spark A.I. innovation in a company of any size
• How to effectively use the unlabelled, unbalanced data sets that abound in business
• How to engineer reusable data and software components to tackle related projects efficiently
• The three distinct types of founders she looks for when she puts together the founding team of an A.I. start-up

Today’s episode touches on a few technical details here and there but the episode will largely be of interest to anyone who’s keen to make the most of A.I. innovation in a commercial organization, whether you happen to have a deep technical background today or not.

Special shout-out to the St. Gallen Symposium (Svenja, Rolf), which Nicole and I discuss our love for (as well as how you can get free flights, accommodation, and access — deadline to apply is Feb 1) starting at the 34-minute mark.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Continuous Calendar for 2022

Added on January 24, 2022 by Jon Krohn.

This article was originally adapted from a podcast, which you can check out here.

All right, so the past two Fridays, I had episodes for you on daily habits. We’ll continue on with that habit series next week, but I’m interrupting the series today to bring you a time-sensitive message.

Back in Episode #482, which aired in June, I provided you with an introduction to continuous calendars — a rarely used, but from my perspective, vastly superior way of viewing your upcoming deadlines relative to the much more common monthly or weekly calendars.

The Receiver-Operating Characteristic (ROC) Curve

Added on January 24, 2022 by Jon Krohn.

In this video, we work through a simple example — with real numbers — to demonstrate how to calculate the ROC Curve, an enormously useful metric for quantifying the performance of a binary classification model.

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.

Data Observability — with Dr. Kevin Hu

Added on January 18, 2022 by Jon Krohn.

This week's guest is the fun and wildly intelligent entrepreneur, Kevin Hu, PhD. Inspired by his doctoral research at MIT, he co-founded Metaplane, a Y-Combinator-backed data observability platform.

In a bit more detail, Kevin:
• Is Co-Founder/CEO of Metaplane, a platform that observes the quality of data flows, looks for abnormalities in the data, and reports issues
• Completed a PhD in machine learning and data science from the Massachusetts Institute of Technology

In this episode, Kevin covers:
• What data observability is and how it can help identify data quality issues immediately as well as more quickly resolve the source of the issue
• His PhD research on automating data science systems using ML
• How he identified the problem his start-up Metaplane would solve
• His experience in Y-Combinator accelerating Metaplane
• Pros and cons of an academic career relative to the start-up hustle
• The surprising complexity of the software tools he uses daily as a CEO
• What he looks for in the data engineers that he hires

This episode gets a little technical here and there but I think Kevin and I were pretty careful to define technical concepts when they came up, so today’s episode should largely be appealing to anyone who’s keen to learn a lot from a brilliant entrepreneur, especially if you’d like to found or grow a data science start-up yourself. Enjoy!

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Daily Habit #2: Start the Day with a Glass of Water

Added on January 17, 2022 by Jon Krohn.

This article was originally adapted from a podcast, which you can check out here.

On Five-Minute Friday last week, I introduced the life-transforming concept of daily habit tracking and, if you‘re keen to try it for yourself, I provided an example spreadsheet to get you up and running quickly on your own daily habit tracking journey. If you check out the YouTube version of last week’s episode, you’ll also see that I screenshared the final part of the episode in order to provide a hands-on demonstration of how to configure my habit-tracking template for whatever habits you’d like to track.

Starting with today’s episode, I’ll be recurringly using Five-Minute Fridays to cover the daily habits I track that are most influential in my life. Perhaps they’ll provide food for thought for you or maybe you’ll even try adopting some of them into your own life.

The Confusion Matrix

Added on January 13, 2022 by Jon Krohn.

This video is a quick introduction to the Confusion Matrix, which thankfully really isn’t all that confusing! Understanding what the Confusion Matrix is is key to an Integral Calculus application coming up shortly in this video series.

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.

Interpretable Machine Learning — with Serg Masís

Added on January 13, 2022 by Jon Krohn.

This week's guest is Serg Masís, an absolutely brilliant data scientist who's specialized in modeling crop yields and climate change. He's also a world-leading author and expert on Interpretable Machine Learning.

Serg:
• Is a Climate & Agronomic Data Scientist at Syngenta.
• Wrote "Interpretable Machine Learning with Python", an epic hands-on guide to techniques that enable us to interpret, improve, and remove biases from ML models that might otherwise be opaque black boxes.
• Holds a Data Science Masters from the Illinois Institute of Technology.

In this episode, Serg details:
• What Interpretable Machine Learning is.
• Key interpretable ML approaches we have today / when they're useful.
• Social and financial ramifications of getting model interpretation wrong.
• What agronomy is and how it’s increasingly integral to being able to feed the growing population on our warming planet.
• What it’s like to be a Climate & Agronomic Data Scientist day-to-day and why you might want to consider getting involved in this fascinating, high-impact field.
• His productivity tips for excelling when you have as many big commitments as he does.

Today’s episode does get technical in parts but Serg and I made an effort to explain many technical concepts at a high level where we could, so today’s episode should be equally appealing to both practicing data scientists and anyone who’s keen to understand the importance and impact of interpretable ML or agronomic data science. Enjoy!

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Deep Learning's Neurobiology Origins

Added on January 13, 2022 by Jon Krohn.

Interested in understanding what Deep Learning is? Here's a ten-minute talk I gave last month that visually explains the approach, how it originated in neuroscience research, and how it's now leading the A.I. revolution.

Thanks to Kate Strachnyi and Ravit Jain for organizing the fun "Holiday Book Party" that I gave this talk at. And thanks to Aglae Bassens for the stunning illustrations I used throughout.

Daily Habit #1: Track Your Habits

Added on January 10, 2022 by Jon Krohn.

This article was originally adapted from a podcast, which you can check out here.

In September 2016, Konrad Kopczynski — who happened to be the guest on episode #465 of the SuperDataScience podcast — introduced me to the idea of daily habit tracking.

I appreciate that it’s easy to throw around an expression like “life-changing”, but tracking my habits every day really has been a dramatically life-changing experience. When you wake up every morning and report honestly to yourself on whether you did or didn’t perform a particular good or bad habit yesterday, you open up your eyes to who you really are in a way that our minds otherwise trick us into ignoring or exaggerating.

Binary Classification

Added on January 10, 2022 by Jon Krohn.

Last week, I kicked off a series of YouTube videos on Integral Calculus. To provide a real-world Machine Learning application to apply integral calculus to, today's video introduces what Binary Classification problems are.

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.

Data Science Trends for 2022

Added on January 10, 2022 by Jon Krohn.

Happy New Year! To kick it off, this week's episode features the marvelous Sadie St. Lawrence predicting the data science trends for 2022. Topics include AutoML, Deep Fakes, model scalability, NFTs, and data literacy.

In a bit more detail, we discuss:
• How the SDS podcast predictions for 2021 panned out (pretty well!)
• The AutoML tools that are automating parts of data scientists’ jobs.
• The social implications of Deep Fakes, which are becoming so lifelike and easy to create.
• Principles for making A.I. models infinitely scalable in production.
• The impact of the remote-working economy on data science employment.
• Practical uses of blockchain and non-fungible token tech in data science.
• Improving the data literacy of the global workforce across all industries.

Sadie:
• Has taught over 300,000 students data science and machine learning.
• Is the Founder and CEO of WomenInData.org, a community of over 20,000 data professionals across 17 countries.
• Is remarkably well-read on the future of technology across industries.

This episode is relatively high-level. It will be of interest to anyone who’d like to understand the trends that will shape the field of data science and the broader world not only in 2022, but also in the years beyond.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

What I Learned in 2021

Added on January 2, 2022 by Jon Krohn.

This article was originally adapted from a podcast, which you can check out here.

For the final episode of 2017, of 2018, of 2019, and of 2020, Kirill Eremenko — the founding host of the SuperDataScience podcast — provided a long guest episode that he called “1-on-1 with Kirill: What I Learned in the Past Year”.

For today’s episode, to cap off 2021, I’m going to do something similar. Instead of a long 1-on-1 guest episode, I’m doing it as a shorter Five-Minute Friday episode because I had too many exciting guest interviews in the recording pipeline that I couldn’t wait to publish and share with you.

Like Kirill in his annual recaps, I’m going to go over a list of specific lessons. At the end of 2021, I’ve got five:

Consistency Leads to Results
Delegation is the Key to Successful Scaling
Remote Working Works
Real-Life Smiles Are Essential
All Work and No Play Makes me a Dull Boy

Consistency Leads to Results

All right, so let’s start with Consistency Leads to Results. This one is the kind of advice that sounds obvious, yet we often struggle to actually do it. If you want great results at some challenging pursuit, it ain’t gonna come easy. However, if you work at it with unwavering consistency, the gains do eventually come surprisingly easily and the gains build on themselves rapidly.

To illustrate this, I’m going to give you two examples with hard data to back me up. The first is on growing an audience and the second is on weightlifting.