Next week, I'm interviewing the monumental Wes McKinney — creator of pandas, co-creator of Apache Arrow, and bestselling author of "Python for Data Analysis" — for a SuperDataScience episode.
Got Qs for him? Tweet them @jonkrohnlearns or send them to me on LinkedIn.
This week's video builds on the preceding ones in my ML Foundations series to advance our understanding of partial derivatives by working through geometric examples. We use paper and pencil as well as Python code.
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.
This week's guest is prominent data scientist and author Dr. Drew Conway. Working at Two Sigma, one of the world's largest hedge funds, Drew leads data science for private markets (e.g., real estate, private equity).
If you aren't familiar with Drew already, he:
• Serves as Senior Vice President for data science at Two Sigma
• Co-authored the classic O'Reilly Media book "ML for Hackers"
• Was co-founder and CEO of Alluvium, which was acquired in 2019
• Advised countless successful data-focused startups (e.g., Yhat, Reonomy)
• Obtained a PhD in politics from New York University
In this episode, he covers:
• What private investing is
• How data science can lead to better private investment decisions
• The differences between creating and executing models for public markets (such as stock exchanges) relative to private markets
• What he looks for in the data scientists he hires and how he interviews them
This is a special SuperDataScience episode because it's the first one recorded live in front of an audience (at the The New York R Conference in September). Eloquent Drew was the willing guinea pig for this experiment, which was a great success: We filmed in a single unbroken take and fielded excellent audience questions.
Listen or watch here.
Five-Minute Friday today is an intro to (deep) reinforcement learning, which has diverse cutting-edge applications: E.g., machines defeating humans at complex strategic games and robotic hands solving Rubik’s cubes.
You can watch or listen here.
We're halfway through my live 14-class "ML Foundations" curriculum, which I'm offering via O'Reilly Media. The first Probability class is on Wednesday with five of the seven remaining classes open for registration now:
• Oct 6 — Intro to Probability
• Oct 13 — Probability II and Information Theory
• Oct 27 — Intro to Statistics
• Nov 3 — Statistics II: Regression and Bayesian
• Nov 17 — Intro to Data Structures and Algorithms
The final two classes will be in December and are on Computer Science topics: Hashing, Trees, Graphs, and Optimization. Registration for them should open soon.
All of the training dates and registration links are provided at jonkrohn.com/talks.
A detailed curriculum and all of the code for my ML Foundations series is available open-source in GitHub here.
My recent videos have detailed how to calculate partial derivatives by hand. In today's, I demo how we can compute them automatically using PyTorch, enabling us to easily differentiate complex equations like ML 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.
This week's guest is the game-changing Dr. Parinaz Sobhani. She leads ML at Georgian — a private fund that sends her "special ops" data science teams into its portfolio companies to accelerate their A.I. capabilities.
In this episode, Parinaz details:
• Case studies of Georgian's A.I. approach in action across industries (e.g. insurance, law, real estate)
• Tools and techniques her team leverages, with a particular focus on the transfer learning of transformer-based models of natural language
• What she looks for in the data scientists and ML engineers she hires
• Environmental and sociodemographic considerations of A.I.
• Her academic research (Parinaz holds a PhD in A.I. from the University of Ottawa where she specialized in natural language processing)
Listen or watch here.
...and thanks to Maureen for making this connection to Parinaz!
Five-Minute Friday today features my 91-year-old grandmother sharing her insightful life philosophy that centers around an analogy of ants building ant hills.
Listen here.
Last week's YouTube tutorial was an epic intro to Partial Derivative Calculus — a critical foundation for understanding Machine Learning. This week's video features coding exercises that test your comprehension of that material.
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.
Expert Rob Trangucci joins me this week to provide an introduction to Bayesian Statistics, a uniquely powerful data-modeling approach.
If you haven't heard of Bayesian Stats before, today's episode introduces it from the ground up. It also covers why in many common situations, it can be more effective than other data-modeling approaches like Machine Learning and Frequentist Statistics.
Today's episode is a rich resource on:
• The centuries-old history of Bayesian Stats
• Its particular strengths
• Real-world applications, including to Covid epidemiology (Rob's particular focus at the moment)
• The best software libraries for applying Bayesian Statistics yourself
• Pros and cons of pursuing a PhD in the data science field
Rob is a core developer on the open-source STAN project — a leading Bayesian software library. Having previously worked as a statistician in renowned professor Andrew Gelman's lab at Columbia University in the City of New York, Rob's now pursuing a PhD in statistics at the University of Michigan.
Listen or watch here.
Five-Minute Friday this week is a high-level intro to the two largest categories of Machine Learning approaches: Supervised Learning and Unsupervised Learning.
Listen or watch here.
Here is my brand-new 30-minute intro to Partial Derivative Calculus. To make comprehension as easy as possible, we use colorful illustrations, hands-on code demos in Python, and an interactive click-and-point curve-plotting tool.
This is an epic video covering a massively foundational topic underlying nearly all statistical and machine learning approaches. I hope you enjoy it!
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.
SuperDataScience SuperStar Hadelin returns to report on his journey from multi-million-selling video instructor to mainstream-film actor — and he details the traits that allow data scientists to succeed at anything.
Hadelin has created and presented 30 extremely popular Udemy courses on machine learning topics, selling over two million copies so far. Prior to his epic creative period publishing ML courses, Hadelin studied math, engineering and A.I. at the Université Paris-Saclay and he worked as a data engineer at Google. More recently Hadelin has written a book called "A.I. Crash Course" and was co-founder and CEO of BlueLife AI.
Today's episode focuses on:
• Hadelin's recent shift toward acting in mainstream films
• The characteristics that enable an outstanding data scientist to excel in any pursuit
• How to cultivate your passion and achieve your dreams
• Bollywood vs Hollywood
• How to prepare for the TensorFlow Certificate Program
• Software modules for deploying deep learning models into production
Listen or watch here.
Five-Minute Friday this week is a high-level introduction to Classification and Regression problems — two of the main categories of problems tackled by Machine Learning algorithms.
Listen or watch here.
Every few months, we begin a new subject in my Machine Learning Foundations course on YouTube and today is one of those days! This video introduces Subject 4 (of 8), which covers Partial Derivatives and Integrals.
This subject-intro video provides a preview of all the content that will be covered in this subject. It also reviews the Single-Variable Calculus you need to be familiar with (from the preceding subject in the ML Foundation series) in order to understand Partial Derivatives (a.k.a. Multi-Variable Calculus).
The thumbnail illustration of my ever-learning puppy Oboe is by the wonderful artist Aglae Bassens.
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.
Very special guest this week! Pieter Abbeel is a serial A.I. entrepreneur, host of star-studded The Robot Brains Podcast, and the world's most preeminent researcher of Deep Reinforcement Learning applications.
As a professor of Electrical Engineering and Computer Science at the University of California, Berkeley, Pieter directs the Berkeley Robot Learning Lab and co-directs the Berkeley A.I. Research Lab.
As an entrepreneur, he's been exceptionally successful at applying machine learning for commercial value. Gradescope, a machine learning company in the education technology space that he co-founded, was acquired in 2018. And the A.I. robotics firm Covariant, which he co-founded more recently, has raised $147 million so far, including raising $80 million in a Series C funding round in July.
In this episode, Pieter eloquently discusses:
• His exciting current research in the field of Deep Reinforcement Learning
• Top learning resources and skills for becoming an expert in A.I. robotics
• How academic robotics research is vastly different from R&D for industry
• Productivity tips
• Traits he looks for in data scientists he hires
• Skills to succeed as a data scientist in the coming decades
He also had time to answer thoughtful questions from distinguished SuperDataScience listeners Serg Masís and Hsieh-Yu Li.
Listen or watch here.
The Five-Minute Friday episode this week is on Imposter Syndrome, including what it is and how to manage it.
Thanks to Nikolay for the episode idea and Micayla for doing most of my homework for it!
Listen or watch here.
Today's brand-new, epic 40-minute YouTube tutorial ties together the preceding 27 Calculus videos to enable us to perform Machine Learning from first principles and fit a line to data points.
To make learning interactive and intuitive, this video focuses on hands-on code demos featuring PyTorch, the popular Python library for Automatic Differentiation.
If you're familiar with differential calculus but not machine learning, this video will make clear for you how ML works. If you're not familiar with differential calculus, the preceding videos in my "Calculus for Machine Learning" course will provide you with all of the foundational theory you need for ML.
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.
This week's guest is THE Jared Lander! He fills us in on real-life communities that support learning about — and effectively applying — open-source statistical-programming languages like Python and R.
In addition, Jared:
• Overviews what data-science consulting is like (with fascinating use-cases from industrial metallurgy to "Money Ball"-ing for the Minnesota Vikings)
• Details the hard and soft skills of successful data-science consultants
• Ventures eloquently into the age-old R versus Python debate
Jared leads the New York Open Statistical Programming Meetup, which is the world's largest R meetup — but it also features other open-source programming languages like Python — for talks from global leaders in data science and machine learning. And Jared runs the R Conference, which is approaching its seventh annual iteration next week, Sep 9-10.
Jared also wrote the bestselling book "R for Everyone" and teaches stats at both Columbia University in the City of New York and Princeton University. And none of the massive responsibilities that I've just mentioned are Jared's day job! Nope, for that he's the CEO and Chief Data Scientist of Lander Analytics, a data-science consulting firm.
Watch or listen here.
P.S.: Jared is kindly providing 20% off admission to next week's R Conference off using promo code SDS20. See rstats.nyc for more details, including the first-ever live episode of SuperDataScience (with Drew Conway as guest)!
Episode 500 of the SuperDataScience podcast is live today! For this special occasion, world-class yogi Jes Allen guides us through a full, deep session of Yoga Nidra — a centering and transformative meditation-like experience.
I'm so excited to share this practice with you and can't wait to hear what you think of it! Thank you to all of you listeners — as well as of course SuperDataScience founder / 400-plus-episode-host Kirill Eremenko — for bringing this podcast to where it is today. And none of this would be possible without the hundreds of inspiring guests we've had over the years, the indefatigable show manager Ivana, and the awesome production team: Mario, Jaime, and JP.
I am honored and grateful to be able to serve all of you and walk alongside you in your data-science career journey. Keep on rockin'! 🎸
You can listen to or watch the episode here.
