I ask every guest on the SuperDataScience show for a book recommendation. Now it's my turn! In this post, I discuss what I love about my favorites in both the fiction and non-fiction realms 📚
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Intro to Integral Calculus
Today’s video is a quick intro to Integral Calculus, the other branch of the mathematical field alongside Differential Calculus (which was introduced in the preceding video, released on Monday).
The YouTube playlist for my "Calculus for Machine Learning" course is here.
Learning Deep Learning Together
I'm joined today by Prof. Konrad Körding of the University of Pennsylvania, a world-leading researcher on links between biological neuroscience and A.I. He also leads Neuromatch Academy, a super cool group-based deep learning school.
Neuromatch is an innovative, hands-on program for learning deep learning that matches students with similar interests, languages, and time zones into tight-knit study teams. This matching approach is wildly successful, with 86% of students completing the program, compared to a 10% industry average.
In the first half of the episode, we go over the details of the Neuromatch curriculum, providing you with a survey of all of the state-of-the-art deep learning approaches. The second half is a mind-blowing exploration of the limits of artificial neural networks today and how incorporating more biological neuroscience may enable machines to develop artificial general intelligence (AGI) — i.e., machines that learn as well as humans do.
Listen or watch here.
Intro to Differential Calculus
New YouTube video out today that uses colorful visual analogies to introduce what differential calculus is. The next video, coming out on Thursday, will introduce integral calculus, the other main branch of the mathematical field.
My apologies for the (temporary!) crappy thumbnail 😂 — I had to fill in for someone who hasn't been feeling well (he's getting better, don't worry) and make it myself.
My Calculus for ML YouTube playlist is here.
Linear Algebra for Machine Learning: Complete Math Course on YouTube
At a high level, my ML Foundations content can be broken into four subject areas: linear algebra, calculus, probability/stats, and computer science. The first quarter of the content, on linear algebra, stands alone as its own discrete course and is available on YouTube today.
The playlist for my complete Linear Algebra for Machine Learning course is on YouTube here. There are a total of 48 videos in the course, each of which is provided in this blog post. Click through for all the detail!
Read MoreThe History of Data
Last month, I thought I was taking a risk by doing an episode on the History of Algebra, but it was an unusually popular episode! To follow up, today's Five-Minute Friday is on the four-billion-year History of Data — hope you enjoy it 😁
You can watch or listen here.
New Series: Calculus for Machine Learning! Plus a New Video Schedule
With all of the Linear Algebra of my Machine Learning Foundations series already available on YouTube, it's time for a new subject: Calculus! The first Calculus video — an intro to the subject — is live today!
Up until now, I've been releasing my YouTube videos in thematic blocks, however I'm going to try something new now to set clearer delivery-timeline expectations with you. Starting with this video, I'll release a new YouTube video every Monday and every Thursday.
My Calculus for ML YouTube playlist 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.
The first quarter of the series covered Linear Algebra. We're now kicking off the second quarter, on Calculus. The third quarter will be on Probability and Statistics. The final one will be on Computer Science (Data Structures, Algorithms, and Optimization).
Translations of Deep Learning Illustrated
My book, Deep Learning Illustrated, is now available in 🇷🇺 Russian, 🇩🇪 German, and 🇰🇷 Korean.
Thanks to Kathrin Lichtenberg and Haesun Park for wonderfully detailed and thoughtful translations. Japanese, Traditional Chinese, and Simplified Chinese translations are also in the works.
The book is packed with lucid illustrations by the talented artist Aglae Bassens, full-color equations, and easy-to-follow code. This approach sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn.
More detail and discounted purchasing options are available at deeplearningillustrated.com (links to translations are listed at the bottom).
High-Impact Data Science Made Easy
Today, the wise Noah Gift weighs pros and cons of data science learning options (university degrees vs online certifications; full-time vs on-the-job) as well as how MLOps can quickly make you exponentially more impactful.
Noah has worked in countless technical leadership roles. He held the roles at companies ranging from tech start-ups he founded to prominent institutions like ABC, Caltech, and AT&T. Today, Noah’s founder of a consultancy called Pragmatic AI Labs — and he devises and teaches data science curricula at several of the most prestigious American universities, including Duke, Northwestern, and Berkeley. He has written eight books, including the bestselling Python for DevOps and the forthcoming Practical MLOps.
On top of all that incredible background, Noah has rich, well-formed life philosophies, which we dig into into detail. I learned a ton from him during this episode, and have been thinking about concepts we discussed time and again since filming. I highly recommend checking the episode out!
You can listen or watch here.
Good vs. Great Data Scientists
What separates a good data scientist from a great one? I asked this on Twitter recently and received hundreds of replies — some witty, others very thoughtful. For today's Five-Minute Friday episode, I review and summarize the thread.
The Tweet has had a crazy 7k engagements on 220k impressions so far — evidently it's a topic that lots of people have an opinion on. I highlighted some of my favorite individual replies in the video, including those from Martin Goodson, Chris Albon, Brandon Rohrer, Chelsea Parlett-Pelleriti, and Isabella Ghement.
What do you think? Let me know if I missed anything important!
You can listen to or watch my video summary here, or you can click through for the blog-post version.
The full Twitter thread is here if you'd like to dig through the entirety of the collective wisdom.
Read MoreAnalytics for Commercial and Personal Success
I believe the easiest way to attain success — in personal or professional endeavors alike — is to rigorously track and analyze the right data. Konrad Kopczynski is a master on this topic and he joins me for this week's guest episode.
Whether you're developing machine learning models, maximizing your company's profitability, or tackling a full-length Ironman triathlon, if you're disciplined about data collection, tracking, and reflection, you can iterate, improve, and achieve your dream state. This is a central tenet of my life and much of my ideology on it has been influenced by my near-decade-long friendship with Konrad.
Konrad is the founder and managing partner of impakt Advisors, a consultancy that specializes in harnessing data for, well, impact. They structure the various data sources into thoughtfully constructed data warehouses and then layer on top analytics, data-science models, and visualizations to enable real-time reports, dashboards, and predictions across all the key areas of a business, including digital marketing, customer retention, behavioral segmentation, and profit margin.
Listen to or watch here.
A.I. vs Machine Learning vs Deep Learning
"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 MoreTime-Series Analysis
Matt 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!
Matrix of Data Voices
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 😉
Our Machine Learning Company, untapt, is Acquired!
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.
It Could Be Even Better
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 MoreMLOps for Renewable Energy
You'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 Linear Algebra quarter of my Machine Learning Foundations series is complete!
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.
The History of Algebra
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.
Tackling Climate Change with ML
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.