Binary Classification

Last week, I kicked off a series of YouTube videos on Integral Calculus. To provide a real-world Machine Learning application to apply integral calculus to, today's video introduces what Binary Classification problems are.

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.

Data Science Trends for 2022

Happy New Year! To kick it off, this week's episode features the marvelous Sadie St. Lawrence predicting the data science trends for 2022. Topics include AutoML, Deep Fakes, model scalability, NFTs, and data literacy.

In a bit more detail, we discuss:
• How the SDS podcast predictions for 2021 panned out (pretty well!)
• The AutoML tools that are automating parts of data scientists’ jobs.
• The social implications of Deep Fakes, which are becoming so lifelike and easy to create.
• Principles for making A.I. models infinitely scalable in production.
• The impact of the remote-working economy on data science employment.
• Practical uses of blockchain and non-fungible token tech in data science.
• Improving the data literacy of the global workforce across all industries.

Sadie:
• Has taught over 300,000 students data science and machine learning.
• Is the Founder and CEO of WomenInData.org, a community of over 20,000 data professionals across 17 countries.
• Is remarkably well-read on the future of technology across industries.

This episode is relatively high-level. It will be of interest to anyone who’d like to understand the trends that will shape the field of data science and the broader world not only in 2022, but also in the years beyond.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Integral Calculus - The Final Segment of Calculus Videos in my ML Foundations Series

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.

How to Found, Grow, and Sell a Data Science Start-up

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.

Exercise on Higher-Order Partial Derivatives

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.

Fusion Energy, Cancer Proteomics, and Massive-Scale Machine Vision — with Dr. Brett Tully

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.

Higher-Order Partial Derivatives

This 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.

Data Science at the Command Line

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.

Backpropagation

This 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.

A.I. Robotics at Home

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!

The Gradient of Mean Squared Error

For 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.

Gradient Descent (Hands-on with PyTorch)

In 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.

Hurdling Over Data Career Obstacles

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.

Deep Learning Battle: Pytorch vs. Tensorflow

When 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.

The Gradient of Quadratic Cost

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.

Open-Source Analytical Computing (pandas, Apache Arrow)

The legend Wes McKinney is this week's guest! He details the genesis of the ubiquitous pandas library, the forthcoming edition of his bestselling book, and how Apache Arrow brings analytics into the distributed computing era.

Wes:
• Created pandas, the industry-standard Python library for data analytics
• Co-created Apache Arrow, a language-agnostic open-source library for efficient analytics on modern distributed CPUs and GPUs
• Wrote the classic O'Reilly Media desk reference "Python for Data Analysis"
• Has worked as technical expert at prestigious firms like Cloudera, RStudio PBC, Two Sigma, and AQR Capital Management
• Today serves as co-founder and CTO of Voltron Data

In this episode, Wes takes us on a technical deep-drive through:
• The creation story of his now-ubiquitous pandas library
• A sneak peek at the third edition of his international-bestselling book
• What the Apache Arrow project is and why it's poised to revolutionize the data science and software industries
• The software and hardware tools that he uses daily to be such an epically productive software developer and entrepreneur
• Responses to great questions by listeners Daniel, David, Doug, and Brett

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.