After several months of publishing videos on the Differential branch of Calculus, with today's video we turn our focus toward the *Integral* branch. As ever, applications of this math to Machine Learning remain central.
We publish a new video from my "Calculus for Machine Learning" course to 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.
This week's guest is terrifically witty Austin Ogilvie, a prodigiously successful data science entrepreneur. He was founder and CEO of the iconic start-up Yhat and is now founder/CEO of rapidly-scaling Laika.
Austin:
• Was the Founder and CEO of Yhat, a start-up that built tools for data scientists and had a loyal cult following in the data science community.
• In 2018, Yhat was acquired by Alteryx, an analytics automation company.
• More recently he founded Laika, a “compliance-as-a-service” company that dramatically improves your capacity to sell your products.
• Laika last month closed a $35m Series B funding round, bringing the total raised by the firm over two years to a staggering $48m.
In this episode, Austin describes:
• His journey from an arts degree studying foreign languages to teaching himself programming and machine learning, and then bootstrapping a data science start-up into a respected brand and acquisition target.
• His unique take on what makes a great data scientist.
• The hands-on data science tools he finds great value in coding with day-to-day as the founder and CEO of fast-growing tech start-ups.
• His practical tips for growing into a successful technical founder, whether you have a technical background yourself today or not.
Today’s episode will be of great interest to anyone who’s interested in founding, growing, and/or successfully exiting a tech start-up, particularly if you’re thinking of incorporating data or A.I. elements.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Five-Minute Friday this week is a holiday greeting from me. Enjoy :D
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
To cap off an epic four-month sequence of videos on Partial-Derivative Calculus, today's YouTube video features an exercise on Higher-Order Partial Derivatives. Next week, a new topic area begins: Integral Calculus!
We publish a new video from my "Calculus for Machine Learning" course to 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.
This week's guest is Dr. Brett Tully, who leverages his rich cross-domain experience to detail how data science is applied to the fields of nuclear fusion energy, cancer biology, and massive-scale aerial machine vision.
In today’s episode, Brett details for us:
• What nuclear fusion is, how harnessing fusion power commercial could be a pivotal moment in the history of humankind, and how data simulations may play a critical role in realizing it
• How the study of the healthy proteins versus the proteins present in someone with a particular cancer type is accelerating the availability and impact of personalized cancer treatment
• What it means to be a Director of A.I. Output Systems and how this role fits in with other A.I. activities in an organization, such as model research and development
• His favorite software tools for working with geospatial data
• His tricks for the effective management of a team of ML Engineers
• His take on the big A.I. opportunities of the coming years
Brett:
• Is the Director of A.I. Output Systems at Nearmap, a world-leading aerial imagery company that uses massive-scale machine vision to detect and annotate vast images of urban and rural areas with remarkable detail
• As the Head of Simulation at First Light Fusion, he developed state-of-the-art data simulations that could be a key stepping stone toward enabling commercial nuclear fusion reactors
• As the Group Leader of Software Engineering at a major research hospital, he worked to characterize the differences in the proteome — the complete catalog of proteins in your body — between cancer patients and healthy individuals
• As a PhD student at the University of Oxford, he simulated how the cerebrospinal fluid present in our brains flows in order to better understand neurological abnormalities
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
This article was originally adapted from a podcast, which you can check out here.
Recently, I had dinner with my wonderful friend Jake Zerrer, who’s a Senior Software Engineer at Flexport, a logistics and supply chain start-up based in San Francisco.
Conversation with Jake is never dull, but I particularly enjoyed a part of the conversation where he brought up an idea for framing problems: He described this framework on the basis of mutable versus immutable conditions.
Read MoreThis week's YouTube video introduces higher-order derivatives for multi-variable functions, with a particular focus on the second-order partial derivatives that abound in machine learning.
We publish a new video from my "Calculus for Machine Learning" course to 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.
This week's guest is Dr. Jeroen Janssens, a global expert and bestselling author on effectively leveraging the Unix command line as a data scientist.
Jeroen:
Wrote the popular book "Data Science at the Command Line", the second edition of which was published by O'Reilly Media in October
Is the Founder Data Science Workshops B.V., which provides hands-on workshops to global orgs such as Amazon and The New York Times
Is Organizer of the Data Science Netherlands Meetup (3000+ members)
Former Assistant Professor at the Jheronimus Academy of Data Science
Has worked as a data scientist for Elsevier, YPlan, and Outbrain
Holds a PhD in A.I. from Tilburg University
In today’s episode, Jeroen details:
Why being able to do data science at the command-line — for example, in a Bash terminal — is an invaluable skill for a data scientist to have
How mastering the command line is the glue that facilitates “polyglot data science”, the ability to seamlessly borrow functions from any programming language in a single workflow
His PhD research on detecting anomalous events in time-series data
Why LaTeX is a great typesetting language to consider using particularly for creating lengthy documents or technical figures that adapt automatically to new data
How his consulting company, Data Science Workshops, grew organically out of his success as an author
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
This article was originally adapted from a podcast, which you can check out here.
I was recently asked if I had a list of favorite A.I. thought leaders that I recommended following on Twitter. I didn’t, but that spurred the idea of today’s episode in which I’ll provide you with my ten picks.
My picks aren’t in a particular order overall, but #1 does happen to be my #1 favorite data scientist, and that’s Andrej Karpathy. Andrej is today the Director of A.I. at Tesla, but I’ve been a huge fan since 2016…
Read MoreThis week's video explains the relationship between Partial-Derivative Calculus and the Backpropagation ("Backprop") approach used widely in training Artificial Neural Networks, including Deep Learning networks.
We publish a new video from my "Calculus for Machine Learning" course to 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.
This week's guest is mad genius Dave Niewinski, who creates A.I.-powered robots for use at home (e.g., cold beer retriever, flame-throwing weed killer) and to teach people A.I. robotics via his popular YouTube channel.
In the episode, Dave covers:
• The specific robotics hardware and open-source software incorporated into his wildest and most well-known robots
• Where machine vision algorithms, particularly deep learning models, are critical for enabling robot functionality
• His tips for folks who’d like to get started in A.I. robotics themselves
• How his interest in robotics led him to founding his Dave's Armoury Ltd. consulting business, which allows him to automate and improve real-world industrial processes with robots
• What excites him most about the societal impact A.I. robotics will have in our lifetimes
Specific robots of Dave's that we detail on the show include ones that:
• Play the sandbag-tossing game "cornhole"
• Defend his machine shop from kids by spraying them with a hose
• Exterminate weeds by throwing flames
• Carve pumpkins for Halloween
• Solve Rubik's cubes
• Use Lego pieces to create 2D artwork
• Race kids at assembling 3D Lego unicorns
• Bring cold beer from the fridge to wherever you are in the house
Thanks to Graham McCormick for introducing me to Dave! I learned so much from him and had such a good time hanging with him "on air".
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com. You can check out the Dave's Armoury YouTube channel here!
This article was originally adapted from a podcast, which you can check out here.
It has been two years since my first book, Deep Learning Illustrated, was published. The years I spent writing the book were filled with persistent darkness and anxiety.
I had a sense of dread that when readers got their hands on the book, they would find catastrophic errors or humiliating gaps in my writing. I feared that some unidentified issue or collection of issues with the book would be so massive that the book would be perceived as a joke or that I would be perceived as a fraud. (I've learned since that such fears are common amongst authors, particularly whilst penning their first book.)
Read MoreFor this week's YouTube video, we manually derive the gradient of Mean Squared Error (popular in ML to quantify accuracy). We then use the Python library PyTorch to obtain the same result and to visualize ML in action.
We publish a new video from my "Calculus for Machine Learning" course to 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.
Meet the brilliant Dr. Peter Bailis. Inspired by research he carried out as a professor at Stanford University, three years ago he founded Sisu to automate enterprise data analytics and the firm has already raised $128m.
In today’s episode, Peter details:
• The revolutionary work being carried out by Sisu: generating automated, actionable reports in minutes that might otherwise take a team of data analysts days
• His guidance for people looking to succeed at growing a tech start-up, particularly if they come from an academic or technical background
• What he looks for in the data scientists and software engineers he hires
• His most important daily tools for developing software productively
• The academic research he carried out at Stanford that’s behind Sisu’s innovative capabilities
Peter:
• Is CEO of Sisu, the firm he founded in San Francisco
• Has raised $128m in venture capital from some of the most prestigious VC firms out there such as Andreessen Horowitz
• Was an assistant professor of computer science at Stanford University, where he’s still an adjunct faculty member today
• Holds several computer science degrees: an undergrad from Harvard University and a PhD from the University of California, Berkeley
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
This article was originally adapted from a podcast, which you can check out here.
Three weeks ago for Five-Minute Friday, I covered the highest-paying programming languages for data scientists based on the results of O’Reilly’s 2021 Data/AI Salary Survey. Two weeks ago, we used Five-Minute Friday to get our definitions of data tools and data frameworks straight so that last week we could dig into the highest-paying data tools and now, this week we’ll wrap this series on compensation up by covering the highest-paying data platforms.
Read MoreIn my preceding YouTube videos, we detailed exactly what the gradient of cost is. With that understanding, today we dig into what it means to *descend* this gradient and fit a machine learning model.
We publish a new video from my "Calculus for Machine Learning" course to 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.
This week's guest is the sensational Karen Jean-François: mathematician, award-winning data analyst, podcast host, and French national champion in the 400m hurdle. She details how to hurdle over data career obstacles.
More specifically, in today’s episode Karen fills us in on:
• How to overcome Imposter Syndrome in the data science industry
• Why you might want to consider becoming a data science manager versus remaining a more specialized individual contributor
• The data tools that she uses regularly
• The productivity and prioritization techniques that enable her to juggle her day job, her thriving podcast, and her world-class athletic pursuits
Karen:
• Manages banking analytics at publicly-listed Cardlytics
• Is the producer and host of the "Women in Data" podcast
• Was recognized last year as one of the "Twenty in Data and Technology"
• Holds degrees in mathematics and computing from Paris-Sud University (Paris XI)
• Was French national champion in the 400m hurdle and bronze medalist in the 100m hurdle
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
This article was originally adapted from a podcast, which you can check out here.
Two weeks ago for Five-Minute Friday, I covered the highest-paying programming languages for data scientists based on the results of O’Reilly’s 2021 Data/AI Salary Survey. Last week we used Five-Minute Friday to get our definitions of data tools and data frameworks straight so that today we could dig into the highest-paying data tools — while next week, in turn, we’ll tackle the highest-paying data platforms. If you get through today’s episode and don’t feel 100% clear about what a data tool is then consider popping back to Episode #522 to clarify.
The most widely-used tool in the survey — used by nearly a third of respondents — was Microsoft’s Excel program for working with data in spreadsheets. Despite its popularity, Excel — along with other click-and-point tools in the survey — was associated with a below-average salary. Specifically, the mean across all respondents was $146k but those who indicated that they used Excel were paid on average $8k/year less at $138k.
Read MoreWhen I teach Deep Learning, the question I get most often is: "Should I be using TensorFlow or PyTorch?" In this recent talk at the DataScienceGO conference, I provide my most thorough and polished response yet.
Thanks to Harpreet Sahota for hosting the session masterfully and leading the audience Q&A at the end.
In this week's video, we derive the Partial Derivatives of Quadratic Cost with respect to the parameters of a simple regression model. This derivation is essential to understanding how machines learn via Gradient Descent.
We publish a new video from my "Calculus for Machine Learning" course to 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.