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.
Filtering by Category: Professional Development
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!
AutoDiff with PyTorch
Over the past month, we've covered all the key rules for differentiating equations by hand. In today's YouTube video, we use PyTorch to differentiate equations automatically and instantaneously.
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.
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.
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.
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.