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.
Filtering by Category: YouTube
The Line Equation as a Tensor Graph
New YouTube video today — it's meaty! In it, we get ourselves set up for applying Machine Learning from scratch by using the popular Python library PyTorch to create a Tensor Graph representation of a simple line equation.
Next week, we'll publish a massive 40-minute video that builds on the Tensor Graph representation introduced this week in order to use Automatic Differential Calculus within a Machine Learning loop and fit a Regression line to data points.
If you're familiar with differential calculus but not machine learning, this pair of videos will fill in all the gaps for you on 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.
Data Meshes and Data Reliability
The fun and brilliant Barr Moses joins me this week to detail for us what organization-transforming Data Meshes are, as well as how to track and improve the "Data Uptime" (reliability) of your production systems.
Barr is co-founder and CEO of Monte Carlo, a venture capital-backed start-up that has grown in head count by a remarkable 10x in the past year. Monte Carlo specializes in data reliability, making sure that the data pipelines used for decision-making or production models are available 24/7 and that the data are high quality.
In this SuperDataScience episode, Barr covers:
• What data reliability is, including how we can monitor for the "good pipelines, bad data" problem
• How reliable data enables the creation of a Data Mesh that empowers data-driven decision-makers across all of the departments of a company to independently create and analyze data
• How to build a data science team
• How to get a data-focused start-up off the ground, generating revenue and rapidly scaled up
In addition, Barr took time to answer questions from listeners, including those from Svetlana, Bernard, and A Ramesh. Thanks to Scott Hirleman for suggesting Barr as a guest on the show and thanks to Molly Vorwerck for ensuring everything ran perfectly.
Listen or watch here.
Tutorial on "Big O Notation"
Brand-new, hands-on intro to "Big O Notation" — an essential computer science concept. "Big O" allows us to weigh the compute-time vs memory-usage trade-offs of all algorithms, including machine learning models.
This YouTube video is a 45-minute, standalone excerpt from my six-hour "Data Structures, Algorithms, and ML Optimization" course, which focuses on code demos in Python to make understanding concepts intuitive, fun, and interactive.
If you have an O'Reilly Media subscription, the full course was recently published here.
If you'd like to purchase the course, Pearson is offering it this week (until August 28th) at a 70% discount as their "Video Deal of the Week". The URL for this unusually deep discount is here.
This "DSA and ML Optimization" course is the fourth and final quarter of my broader ML Foundations curriculum. All of the associated code is available open-source via GitHub.
How Only Beginners Know Everything
For Five-Minute Friday, I review a paradoxical pattern I've noticed in myself and in many early-career data scientists: We think we know everything. That is, until we advance past being novices and discover we're not so great.
Listen or watch here.
AI Recruitment Technology & Deep Learning - Guest Appearance on the Engineered-Mind Podcast
Thanks to Jousef Murad for having me on the popular Engineered-Mind podcast.! Jousef had deeply insightful questions and I enjoyed the experience immensely :)
I spoke with Jousef back in April 2021, where we discussed:
- untapt and how AI powered recruiting works
- My background in neuroscience
- Where to get started when learning ML
- Tips for becoming a deep learning specialist
- What is I’m most excited about in terms of AI
- How I come up with the idea of writing a book
You can listen to the podcast anywhere podcasts are available including Apple Podcasts, Spotify, and Anchor.fm. You can also check out the video directly on YouTube here.
AutoDiff with TensorFlow
PyTorch and TensorFlow are by far the two most widely-used automatic-differentiation libraries. Last week, we used PyTorch to differentiate an equation automatically and instantaneously. Today, we do it with TensorFlow.
(For an overview of the pros and cons of PyTorch versus TensorFlow, I've got a talk here. The TLDR is you should know both!)
A new video for my "Calculus for ML" course published on 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.
Maximizing the Global Impact of Your Career
This week, expert Benjamin Todd details how you can find purpose in your work and maximize the global impact of your career. In particular, he emphasizes how data scientists can exert a massive positive influence.
In this mind-expanding and exceptionally inspiring episode, Ben details:
• An effective process for evaluating next steps in your career
• A data-driven guide to the most valuable skills for you to obtain regardless of profession
• Specific impact-maximizing career options that are available to data scientists and related professionals, such as ML engineers and software developers.
Ben has invested the past decade researching how people can have the most meaningful and impactful careers. This research is applied to great effect via his charity 80,000 Hours, which is named after the typical number of hours worked in a human lifetime. The Y Combinator-backed charity has reached over eight million people via its richly detailed, exceptionally thoughtful, and 100% free content and coaching.
Listen or watch here.
Why The Best Data Scientists have Mastered Algebra, Calculus and Probability
Over the past year, I've published dozens of hours of video tutorials on the mathematical foundations that underlie machine learning and data science. In this talk, I explain *why* knowing this math is so essential.
Thanks to Roberto Lambertini and SuperDataScience for hosting it!
A Brain-Computer Interface Story
For Five-Minute Friday this week, I tried something different: I wrote a short sci-fi story! Let me know if you liked it or hated it and, based on your feedback, I'll either do more of it or consider never doing it again :)
Watch or listen here.
Successful AI Projects and AI Startups
This week, the rockstar Greg Coquillo fills us in on how to get a return on investment in A.I. projects and A.I. start-ups. He also introduces Quantum Machine Learning.
In addition, through responding to audience questions, Greg details:
• Element AI's maturity framework for A.I. businesses
• How A.I. startup success comes from understanding your long-term business strategy while iterating tactically
• How machines typically are much faster than people but tend to be less accurate
(Thanks to Bernard, Serg, Kenneth, Nikolay, and Yousef for the questions!)
Greg is LinkedIn's current "Top Voice for A.I. and Data Science". When he's not sharing succinct summaries of both technically-oriented and commercially-oriented A.I. developments with his LinkedIn followers, Greg's a technology manager at Amazon's global HQ in Seattle. Originally from Haiti, Greg obtained his degrees in industrial engineering and engineering management from the University of Florida before settling into a series of management-level process-engineering roles.
Listen or watch here.
A4N Episode 5: We're on Pause for Now!
In this episode of A4N, I have a special announcement! While the A4N podcast will be going on indefinite hiatus, it is because I am now hosting the SuperDataScience Podcast. If you enjoyed A4N then you're sure to enjoy the SuperDataScience podcast, which publishes twice every week on Tuesdays and Fridays!
You can check it out here.
How to Instantly Appreciate Being Alive
In today's always-connected world, it's easy to get caught up in thought after thought after thought. For Five-Minute Friday this week, here's a trick to jolt yourself into the present and bask in the simple appreciation of being alive.
Listen or watch here.
AutoDiff Explained
New YouTube video live! This one introduces what Automatic Differentiation — a technique that allows us to scale up the computation of derivatives to machine-learning scale — is.
A new video for my "Calculus for ML" course published on 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.
Automatic Differentiation – Segment 3 of Subject 3, "Limits & Derivatives" – Machine Learning Foundations
Automatic Differentiation is a computational technique that allows us to move beyond calculating derivatives by hand and scale up the calculation of derivatives to the massive scales that are common in machine learning.
The YouTube videos in this segment, which we'll release every Wednesday, introduce AutoDiff in the two most important Python AutoDiff libraries: PyTorch and TensorFlow.
My growing "Calculus for ML" course is available on YouTube here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Intro to Regression Models – O'Reilly Live Lessons
My new 80-minute intro to Regression Models is up on YouTube! It's packed with hands-on code demos in Python-based Jupyter notebooks to make learning regression intuitive, interactive, and maybe even fun :)
This lesson is an excerpt from my 9-hour "Probability and Statistics for Machine Learning" video tutorial, which is available via O'Reilly here.
All of the code is available open-source via GitHub.
The Power Rule on a Function Chain — Topic 61 of Machine Learning Foundations
This is the FINAL (of nine) videos in my Machine Learning Foundations series on the Derivative Rules. It merges together the Power Rule and the Chain Rule into a single easy step.
Next begins a chunk of long, meaty videos on Automatic Differentiation — i.e., using the PyTorch and TensorFlow libraries to, well, automatically differentiate equations (e.g., ML models) instead of needing to do it painstakingly by hand.
Because these forthcoming videos are so meaty, we're moving from a twice-weekly publishing schedule to a weekly one: Starting next week, we'll publish a new video to YouTube every Wednesday.
My growing "Calculus for ML" course available on YouTube here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Advanced Exercises on Derivative Rules — Topic 60 of Machine Learning Foundations
Having now covered the product rule, quotient rule, and chain rule, we're well-prepared for advanced exercises that confirm your comprehension of all of the derivative rules in my Machine Learning Foundations series.
There’s just one quick derivative rule left after this — one that conveniently combines together two of the rules we’ve already covered — and then we’re ready to move on to the next segment of videos on Automatic Differentiation with PyTorch and TensorFlow.
New videos are published every Monday and Thursday to my "Calculus for ML" course, which is available on YouTube here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
The Chain Rule for Derivatives — Topic 59 of Machine Learning Foundations
Today's video introduces the Chain Rule — arguably the single most important differentiation rule for ML. It facilitates several of the most ubiquitous ML algorithms, such as gradient descent and backpropagation.
Gradient descent and backprop will be covered in great detail later in my "Machine Learning Foundations" video series. This video is critical for understanding those applications.
New videos are published every Monday and Thursday to my "Calculus for ML" course, which is available on YouTube.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub.
The History of Calculus
Y'all seem to love these "History of..." episodes, so for Five-Minute Friday this week, here's another one. It's on the History of Calculus! Enjoy 😄
(Leibniz and Newton, who independently devised modern calculus around the same time, are pictured.)
Listen or watch here.