"A.I.", "Machine Learning", and "Deep Learning" are terms that are often thrown around interchangeably. They shouldn't be! For Five-Minute-Friday this week, I define each of the three terms in straightforward language.
You can watch or listen to the episode here. Or you can expand below to read my blog post version.
Read MoreMatt Dancho joins me on this week's SuperDataScience guest episode, which is dedicated to time-series analysis. We cover what it is (modeling financial data and other quantities that vary over time) as well as the state-of-the-art techniques and tools.
Matt is the founder and CEO of Business Science, an educational platform dedicated to commercial applications of data science. He's a heavy contributor to open-source projects, particularly the Modeltime ecosystem of R packages he devised. Modeltime makes working with (and modeling!) time-series data both tidy and easy.
Listen or watch here!
Honored to be included in the matrix of Data Voices (populated with two nested "for" loops?) by Kate Strachnyi of DATAcated.
I personally know and admire several of these Voices, all of whom have played a role in my development as a data scientist: Matt Dancho, Kirill Eremenko, Ben Taylor, Kirk Borne, Ph.D., Harpreet Sahota, and Kate herself.
There are countless others whom I know by their stellar reputation only but am hopeful to become close with soon... perhaps even as a guest on an upcoming SuperDataScience podcast episode. Keep an eye out 😉
After six years at untapt, I'm delighted to announce that we've been acquired (Times Square announcement pictured)! Being able to work directly alongside the absolute rockstars at GQR Global Markets every day is a dream come true.
GQR is a global talent acquisition firm, named one of the fastest-growing in each of the last five years — including, remarkably, experiencing hyper-growth through the past pandemic year. By marrying untapt's best-in-class data science and engineering with GQR's best-in-class professional services, we expect further hyper-growth together for many years to come.
Congratulations to everyone on the untapt team, who are all now welcomed into the Wynden Stark family. Massive thanks to everyone on the current team for their relentless innovation and dedication, as well as to the many others who've been with us previously and helped us grow to the extraordinary place we find ourselves today.
This acquisition means that untapt can make a greater impact than ever before much more rapidly than ever before. We have tons of exciting developments in the works — I can't wait to share them with you over the coming months!
Read the full press release here.
When something positive happens in my life, my mind tends to jump to the negative flipside right away. To fend this off, I now repeat to myself "It Could Be Even Better" when something good happens. The mental impact has been sublime.
For this week's Five-Minute Friday episode, I provide specific examples across my professional, fitness, and personal lives where repeating this "It Could Be Even Better" mantra has proved game-changing. If you too slip into negative thinking when something good happens, perhaps it's a simple trick that'll work for you too!
Watch the video above or click through for the full post.
Read MoreYou're in for a treat with Samuel Hinton! The first half of this week's guest episode is a mind-blowing journey on our ever-expanding universe. The second half is on Machine Learning Operations and how Sam uses it to shift the world to clean energy.
Sam is a witty polymath. He's a data scientist, astrophysicist, software engineer, former Survivor contestant, beloved online instructor, and most recently a fiction writer. In his role as Data Scientist at the Arenko Group, a renewable energy company, he leads MLOps, enabling efficient experimentation and productionization of ML models.
Listen or watch here.
The final eight linear algebra videos from my Machine Learning Foundations series are live today! Having covered the fundamentals of linear algebra theory in the preceding videos, we can now apply the theory to ML techniques like data compression, regression, and classification.
The eight new videos are:
It's been an epic personal journey to here. Starting with the first linear algebra video in July of last year, there are now a total of 48 videos in my ML Foundations series — over seven hours of content that constitute the first quarter of the series. Up next are several dozen videos on calculus, which will form the second quarter of content. (Probability/stats will be the third quarter and computer science the fourth.)
The playlist for my entire ML Foundations series is here. The series is full of hands-on demos in Python (particularly the NumPy, TensorFlow, and PyTorch libraries) and all of the code is available open-source in GitHub.
Algebra lies at the core of all modern data science approaches. We've been working on it for a while though — it's at least 3900 years old! For Five-Minute Friday today, I cover the Babylonian genesis of the field through to today's ML applications.
Listen or watch here.
Vince Petaccio II joins me on the SuperDataScience podcast this week to detail how individuals in general — and data scientists in particular — can make a meaningful difference in the fight against climate change.
Particular green machine learning applications we covered include:
• Optimizing energy delivery
• Precision agriculture and vertical farming
• Identification of misinformation
• Climate modeling
Vince is a data scientist at Amazon Web Services (AWS), a sorely-missed former colleague of mine at untapt/GQR, and a brilliantly articulate advocate for climate action through his work as a volunteer lobbyist for the Citizens' Climate Lobby.
Listen or watch here.
The Data Community Content Creator Awards are now accepting nominations. If you could please take a couple of seconds to visit this link and nominate me for:
Category 2 for my YouTube channel
Category 7 for the SuperDataScience podcast I host
Category 8 for my book, Deep Learning Illustrated
…I’d greatly appreciate it!
I also hope to see you at the Data Community Content Creator Awards themselves on April 27th, 2021 — I’m already all dressed up!
Today’s Five-Minute-Friday episode is a behind-the-scenes tour of a professional studio configured for filming video tutorials. Specifically, we had just wrapped filming a ~10-hour series on Probability and Statistics for Machine Learning.
I’ve been fortunate to work with Erina Sanders and Guillaume Rousseau at Production Central Studios and Stages since 2017, including on my bestselling Deep Learning with TensorFlow videos. In this video you meet Erina and Guillaume — and hear about how they make the magic happen!
You can listen or watch here.
(And yes, I know! I learned my lesson and will now film in landscape mode not portrait on my phone!) 🤦♂️
This week, the inspiring Harpreet Sahota joins me on the SuperDataScience show to fill you in on how to land your data science dream job — whether it’s a more senior role or your first foray into the field.
Harpreet is an eminent contributor to the data science community. It's unbelievable how much he gives to world each week. He:
Hosts the The Artists of Data Science podcast
Is the principal mentor at Data Science Dream Job
Hosts weekly office hours that anyone can drop in on
With Kate Strachnyi, founded and hosts the inaugural (and forthcoming) Data Community Content Creator Awards
Despite all of the above, Harpreet's remarkably humble — and capital-S Stoic! Oh, and he has a day job too! He's the lead data scientist at Price Industries, a global industrial leader in HVAC manufacture, and in the episode he details for us data models he's built for them.
You can listen (or watch) here.
I’ve experimented with a lot of productivity techniques and quantitatively track their effectiveness every day. One of my long-standing favorites is the Pomodoro Technique to eke creative, highly focused work out of minimal effort 🍅
You can listen to or watch today's Five-Minute-Friday episode on the Pomodoro Technique here.
In addition to Pomodoros, some other techniques I've covered on the podcast are mindfulness (here and here) and a number popularized by James Clear over the years (see here).
This week, Horace Wu fills us in on how to bootstrap an ML start-up — without outside capital while still earning an income! We also cover how AI can advance the legal sector and how to pivot your career from services to start-up founder.
Syntheia, the latest company Horace founded, augments human lawyers with the power of the (typically millions) of historical documents at their firm. Using a combination of NLP and machine vision (then surfaced in Microsoft Word using javascript), Syntheia provides lawyers with suggestions on tricky points in contracts and automates the generation of clauses.
Prior to founding Syntheia, Horace was an attorney at top firms in Australia and the US. At Syntheia, he works closely with his team of data scientists and developers to solve complex problems for the legal profession. Especially for a supposedly non-technical founder, he displays a stunning depth of knowledge of NLP models and full-stack model deployments.
You can listen and watch here: superdatascience.com/455
We had a four-day studio session Thursday through Sunday filming what will be ~10 hours of interactive videos on Probability and Statistics for Machine Learning. It was a marathon, but it was also a lot of fun — and I think you'll love the content 😃
On next week's Five-Minute-Friday episode of the SuperDataScience podcast, I'll be providing you with a backstage tour. We shot at Production Central Studios and Stages in Manhattan's Union Square. Thanks as always to Erina Sanders for producing masterfully and Guillaume Rousseau for assisting her impeccably.
All of the Python code is available open-source in GitHub today.
The videos themselves will appear in the O'Reilly learning platform in a couple of months, with thanks to Pearson's Debra Williams for bringing another project concept of mine to life.
This episode was adapted from a post that I wrote for the GQR blog.
In today’s Five-Minute Friday episode, I continue last week’s theme. We overview exponentially accelerating technological progress and examine today’s augmentation of human intelligence: the first babysteps of the AI revolution.
In Part 3, coming next month, we'll speculate on how data and automation will dramatically change work and life over the coming decades.
Listen or watch here.
In this week's episode, Stephen Welch joined me to discuss 10 big global problems worth trying to solve with data science and machine learning. Perhaps you'll be inspired to tackle one or two yourself!
2020 presented a lot of challenges to a lot of folks around the world. For Stephen, the challenging year was compounded by intense delivery targets for him and his team on a machine vision-enabled system that automatically detects defective industrial products. After delivering this fascinating edge-deployed software into production, Stephen was able to finally come up for air and he reflected on what's most important in one's life — culminating in his list of 10 global problems worth solving.
Stephen is VP of Data Science at Mariner, where he leads a team developing deep learning-based solutions to manufacturing problems. Prior to working with Mariner, Stephen was VP of Machine Learning at Autonomous Fusion, a firm specialized in self-driving cars. He’s also an adjunct professor at University of North Carolina at Charlotte and the creator of the gorgeous math-focused YouTube channel WelchLabs, which has over 200k subscribers and 10M views.
You can listen or watch here.
Six new YouTube videos from my Machine Learning Foundations series are live today! These round out a thematic segment of linear algebra videos focused on matrix determinants, eigenvectors, and eigenvalues.
The specific videos are:
Up next will be a few videos that focus on real-world, hands-on applications of linear algebra to ML problems, including with singular value decomposition, Moore-Penrose pseudoinversion, and principal component analysis. That will wrap up all of the linear algebra content in the series and then we'll move on to calculus!
The playlist for my entire Machine Learning Foundations series is here.
The series is full of hands-on demos in Python and all of the code is available open-source in GitHub.
This podcast episode is based on a blog post I published for GQR. See here.
For millennia, you might not have witnessed a single innovation in your lifetime. In the 20th Century, any job changed dramatically. In the coming decades, automation will dramatically change all aspects of life many times over.
In this week's FiveMinuteFriday, I examine this acceleration — this staggering pace of progress — through the lens of the recruitment industry that is facilitating a lot of the change.
Listening and viewing options here.
Dan Shiebler has a pretty unambitious hobby: a full-time University of Oxford PhD 😂! Dan researches Category Theory, a math branch that categorizes items by their behavior. By day, he's Staff Machine Learning Engineer at Twitter.
In this week's SuperDataScience episode, Dan explains to me how relatively pure mathematical research translates to improving real-world ML applications.
We also discuss:
• How Twitter labels huge datasets
• How Revenue Science can boost ad performance
• What the responsibilities of a staff software engineer at a big tech company are
• What skills are sought after in data science hires at Twitter
Listening and viewing options here.
