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.
Filming ""Data Structures, Algorithms, and Machine Learning Optimization"" LiveLessons
Silly times on set filming my "Data Structures, Algorithms, and Machine Learning Optimization" videos, which — over 6.5 interactive hours — introduce critical Computer Science concepts for ML and Data Science.
These videos were recently published in the O'Reilly Media platform.
These CS-focused videos are the fourth and final quarter of the subject areas covered in my broader "ML Foundations" curriculum — the first three being Linear Algebra, Calculus, and Probability. All of the code from the curriculum is available open-source in GitHub.
And my "Math for ML" playlist on O'Reilly captures all of the videos in this curriculum in one place.
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.
Bringing Data to the People
This week's guest is super-cool Anjali Shrivastava. Anjali makes data accessible and broadly appealing by analyzing pop culture — from TikTok mansions to Star Wars timelines — in her fun and creative YouTube videos.
Anjali is an expert in data-science visualization. She has used this skill set to engineer visualizations of data in production systems in a number of roles and recently took up a data science role at the lab technology giant Thermo Fisher Scientific.
We dig into her technical expertise, including her favorite software tools and applications for viz. We also discuss Anjali's mission to bring a face to data, which she accomplishes through journalism as well as through her brilliant and fun "Vastava" YouTube channel.
Anjali holds dual degrees from the prestigious University of California, Berkeley in data science, as well as in industrial engineering and operations research. A recent graduate, she fill us in on what a data science degree curriculum is like at a top university like Berkeley, as well as how anyone can access their world class data science lectures online.
Listen or watch here.
The World is Awful (and it’s Never Been Better)
Feel like the world is kinda poopy? Well, it is! BUT, covid pandemic not withstanding, it's also WAY better than ever before. I articulate this idea with data and charts for this week's Five-Minute Friday episode.
Thanks to Benjamin Todd for pointing me in the direction of a blog post by Max Roser (founder of Our World in Data) that formed the basis of this podcast episode.
Watch or listen 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.
R in Production
Dutch national-podium-level powerlifter Veerle van Leemput joins me this week to detail how R is not only an option for production, but may in fact be the *best* production option if data models are central to your application.
Over the course of the episode, Veerle runs down for us her favorite R tools for:
• Data gathering
• Model development
• Deployment into production systems
Veerle has held a number of data-science leadership roles at Dutch companies. She now serves as Managing Director and Head of Data Science at Analytic Health, a London-based firm that builds data-centric software for the healthcare industry. And she was silver medalist in the 57kg class of the 2021 Dutch national powerlifting championships with a total of 335kg (~739 pounds) across the back squat, bench press, and deadlift.
Listen or watch 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.
Say No to Pie Charts
Public Service Announcement for this week's Five-Minute Friday: Don't use pie charts! (Nor, in almost all circumstances, ANY circular chart!)
Listen or watch here.
DataScienceGo This Weekend
The DataScienceGO conference is this weekend — registration for Friday and Saturday is 100% free! I'm speaking Saturday on the pros and cons of TensorFlow vs PyTorch for training and deploying deep-learning models.
Awesome speakers — whom you may already be familiar with from recent SuperDataScience episodes — include:
• Erica Greene (episode # 435)
• Harpreet Sahota (# 457)
• Andrew Jones (# 483)
I don't (yet!) personally know the other speakers pictured here but their weighty reputations precede them and I'm looking forward to getting to know them better over the course of the weekend: Gabriela de Queiroz, Karen JEAN-FRANCOIS, Yudan Lin, Ken Jee, and Danny Ma.
Free registration here!
Monetizing Machine Learning
This week's guest is the legendary Vin Vashishta! Vin details his A.I. commercialization strategy, which allows data science teams and machine learning companies alike to be profitable and successful long-term.
Vin is founder of and chief data scientist at V Squared, his own consulting practice that specializes in monetizing machine learning by helping Fortune 100 companies with A.I. strategy. He's also the creator of several platforms (including The ML Rebellion) for learning about critical skill gaps related to artificial intelligence such as commercial strategy, data science leadership, and model explainability.
In addition to the episode's focus on A.I. strategy, Vin answers questions from SuperDataScience listeners (thanks, Serg, Joe, Daniel, Nikhil, and Michael!), including on:
• Efficiency gains from no-code or low-code machine learning tools
• The biggest skills gaps that data scientists have
• The most disturbing data sets
• Investing in socially beneficial models
• The most challenging problem with commercializing AI
Listen or watch here.
(With thanks to Harpreet Sahota for another stellar guest suggestion!)
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.
The Price of Your Attention
Time is money. Every second of your life is yours to use and one of the options you have is to generate income. You can do this hourly, or, as a data scientist, invest time in a digitally-sharable product with a huge potential ROI.
Listen or watch 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.
TensorFlow vs PyTorch @ DataScienceGo Virtual
The DataScienceGO Virtual conference is coming up next Saturday and it is FREE! I'm giving a talk on TensorFlow vs PyTorch with lots of time for audience questions.
Fixing Dirty Data
My guest this week is the fixer of dirty data herself, the one and only Susan Walsh. We have a lot of laughs in this episode as we discuss how organizations can save substantial sums by tidying up their data.
Susan has worked for a decade as a data-quality specialist for a wide range of firms across the private and public sectors. For the past four years, she's been doing this work as the founder and managing director of her own company, The Classification Guru Ltd. She's also the author of the forthcoming book, "Between the Spreadsheets", and she hosts her own video interview show called "Live from the Data Den".
Listen or watch 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.