Today's YouTube video uses five fun exercises to test your understanding of the derivative rules we’ve covered so far: the Constant Rule, Power Rule, Constant-Multiple Rule, and Sum Rule.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
If you’d happen to like a detailed walkthrough of the solutions to all the exercises in this video, you can check out my Udemy course called Mathematical Foundations of Machine Learning. See jonkrohn.com/udemy
Finished watching last week's Data Community Content Creator Awards and discovered I was one of three finalists for Favorite Technical Author alongside the iconic Aurélien Géron and the category winner Denis Rothman!
This is venerable company and I'm honored — thanks to everyone who voted for me in this category! (The full category name was "Author of Instructional, Technical, or Textbook".)
Many thanks are due to the artist Aglae Bassens, who did an incredible job illustrating "Deep Learning Illustrated", thereby making the book a unique and popular addition to the field. Thanks are also due to co-author Grant Beyleveld who lent his expertise and colorful writing style to many of the topics.
This also couldn't have happened without Debra Williams, Chris Zahn, Julie Nahil, Betsy Hardinger, and many others at Pearson who put extra effort into a book with unusually complex development and production requirements.
Finally thanks again to Kate Strachnyi and Harpreet Sahota for devising and executing the awards ceremony flawlessly.
You can watch the entire ceremony on YouTube here. And you can get more info on my book at deeplearningillustrated.com.
For this week's guest episode, I interrogated Andrew Jones on his data science interview secrets. If you want to improve your interview performance — especially if you're in a data-related career — this episode's for you.
Andrew has held a number of senior data roles over the past decade, including at the tech giant Amazon. In those roles, Andrew interviewed hundreds upon hundreds of data scientists, leading him to create his Data Science Infinity educational program, a curriculum that provides you with the hard and soft skills you need to set yourself apart from other data scientists during the interview process.
Listen or watch here.
Thus far in this set of videos on Differentiation Rules, we’ve covered the Constant, Power, and Constant-Multiple rules. Today's video is on the Sum Rule. On Thursday, we'll have comprehension exercises on all four key rules!
Extremely practical post for you today! It's on the Continuous Calendar, which in my opinion is vastly superior to the standard monthly calendar in every imaginable respect. Click through for more detail.
Continuing my short series on Differentiation Rules, today’s video covers the Constant Multiple Rule. This rule is often used in conjunction with the Power Rule, which was covered in the preceding video, released on Monday.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
I am surprised and utterly delighted to be recognized yesterday with the Data Community Content Creator Award for the "Machine Learning and AI" YouTube category. 🥳
From my perspective, my YouTube channel is still in its early days so while I did not anticipate formal recognition like this perhaps ever, I *certainly* did not so soon after launching the channel. This is a massive, galvanizing signal that I should continue pressing on with this nascent video-creation effort — I absolutely will!
First off, thank you to everyone who voted. This category was apparently one of the tightest races in this "Peoples' Choice"-style awards show, so truly your individual vote may have tipped the award in my favor.
Many thanks are due to Sangbin Lee and Maria Lee, who have edited, produced, branded, and marketed every single video on my channel since day one. My freely-available YouTube content would not exist without them. Thanks as well to Guillaume Rousseau, who recently joined us and dramatically accelerated how quickly we can publish perfectly-edited videos.
Finally, thanks to Harpreet Sahota and Kate Strachnyi who conceived of the DCCCA show and delivered it with the flair, fun, and precision that we'd expect from them!
The entire ceremony is on YouTube here. And a short recap post is here.
My guest this week is Kris Tait, who fills us in on how data and machine learning have transformed — and will continue to transform — marketing, enabling even small firms to effectively target customers and grow their revenue.
In this episode of the SuperDataScience show, we cover:
• What performance marketing is
• The rapidly shifting digital marketing ecosystem, as well as how data and ML can mitigate the risks associated with these changes
• The sweet spot for augmenting human marketers' skills with machines
• How any firm should define metrics to maximize return on marketing investment, thereby ensuring broader commercial success
• The most useful modern data science tools for global digital marketing
Kris is the managing director for the US at Croud - Performance Marketing Agency of the Year, an innovative marketing agency that is driven by data analytics and machine learning algorithms.
Listen or watch here.
On Thursday, I published a video on the Constant Rule, the first video in a series on Differentiation Rules. Today, we continue the series with the Power Rule, arguably the most common and most important of all the rules.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
In recent weeks, I've received several messages from folks struggling to get callbacks for Data Scientist interviews. In reviewing their résumés, I realized there are five specific tips that I highly recommend adhering to.
You can listen or watch here.
This and the next several videos will provide you with clear and colorful examples of all of the most important differentiation rules. We kick these rules off with the Constant Rule.
The derivative rules are critical to machine learning as they allow us to find the derivatives of cost functions. These cost-function derivatives are concatenated into the "gradient" that we descend to allow ML models to learn.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
Biggest milestone of my career so far: Delighted to announce a major capital raise that enables us to launch Nebula, a new machine learning company that reimagines talent management and recruitment.
In April, we announced the acquisition of untapt by Wynden Stark (blog post here). Now, Wynden Stark has completed a massive raise of growth capital from Corbel Capital Partners that makes the launch of Nebula possible. I couldn't be more excited about how this broadens and accelerates our ability to transform human capital industries.
As of today, I'm taking up the position of Chief Data Scientist at Nebula. I feel terrifically fortunate to be making this transition alongside:
untapt's founder, Ed Donner, now Nebula CTO
untapt's CEO, Gareth Moody, now Nebula's head of enterprise sales
Long-standing untapt data scientists Andrew Vlahutin and Grant Beyleveld as well as the many outstanding recent hires to our quickly-growing team
Every single one of untapt's software engineers as well as, again, many brilliant new hires across the engineering and product teams
The world-leading recruiters and commercial leaders from GQR Global Markets, Wynden Stark's service arm, who serve in formal and informal advisory roles on Nebula's products
Congratulations to Wynden Stark's founders Steven Talbot and Hugo Sugden, as well as the rest of the firm, on realizing this significant stepping stone toward the even larger successes to come.
Press release here.
In this week's guest episode, wildly intelligent and meticulously communicative Maureen Teyssier, Ph.D. explains what Knowledge Graphs are, why they're so powerful, and how to grow a flourishing data science team.
In more detail, in today’s episode we cover:
• The theory and applications of Knowledge Graphs, a cool and powerful data type at the heart of much of Maureen’s work at Reonomy
• The data science techniques that Reonomy use to flow data through extremely high-volume pipelines, enabling them to efficiently apply models to their massive data sets
• What Maureen looks for in the data scientists that she hires and the tools and approaches she leverages in order to grow a highly effective data science team
• The differences between data scientists, data analysts, data engineers, and machine learning engineers.
• Maureen’s fascinating academic work in which she used gigantic supercomputers to simulate solar systems and galaxies
Maureen is Chief Data Scientist at Reonomy, a very well-funded New York start-up — they’ve raised over 100 million dollars — that is transforming the world of commercial real estate with data and data science. Prior to working in industry, Maureen was an academic working in the field of computational astrophysics; she obtained her PhD from Columbia University in the City of New York and then carried out research at Rutgers University in New Jersey.
Listen here.
In today's YouTube video, we detail all of the most common notation for derivatives. This lays the foundation for a fun, immediately forthcoming series of videos covering all of the major differentiation rules. Enjoy!
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
I've recently been able to achieve markedly better results than ever before across my personal and professional lives. For Five-Minute Friday, I reflect on five keys to success that may allow achievement of many complex, long-term goals.
You can listen or watch here.
In today's video, we use hands-on code demos in Python to find the slopes of curves with the Delta Method. While finding these slopes, we derive together — from first principles — the most important Differential Calculus formula.
This video is part of a thematic segment of videos on Differentiation. In the forthcoming videos, we’ll cover derivative notation and a series of useful rules for differentiation.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
Getting started in data science? Today's episode is for you! Sidney Arcidiacono is absolutely crushing her first year in the field; we discuss the options for getting started in the field and top tips for early-career success.
Trained as a phlebotomist (blood-sample collection), Sidney was inspired by the potential for machine learning to revolutionize healthcare, so she jumped feet first into a full-time computer science degree at Make School, specializing in the data science track. From no familiarity with code or models just a year ago, Sidney's immersion has paid off: She's now fluent in the modern data science software stack and landed a summer data science internship at GreenLight Biosciences, Inc., an RNA-molecule therapeutics firm (like the Pfizer/BioNTech/Moderna vaccines).
Sidney is terrifically sharp and engaging; I think you'll enjoy hearing from her as much as I did during filming.
Watch or listen here.
In today's video, we use a Python code demo to develop a working understanding of the Delta Method, a centuries-old technique that enables us to determine the slope of a curve.
This video is the first from a thematic segment of videos on Differentiation. In Thursday's video, we'll build on what we covered today to derive — and deeply understand — the most common, most important equation in differential calculus.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
"Peer-driven" learning — where you are formally taught by your coworkers — not only results in team members learning key new skills, but can have added benefits like team bonding, confidence, and innovation. Something to try!
Today's episode is directly inspired by a LinkedIn post by Laura Rodriguez. She tagged me in the post, citing a SuperDataScience episode on communication and relating it to her workplace at ForwardKeys. Thank you, Laura!
