NLP with GPT Architectures (ChatGPT, GPT-4, and other LLMs)

Large Language Models have revolutionized the field of Natural Language Processing, powering mind blowing tools like ChatGPT and GPT-4. Today, we released the recording of a half-day conference I hosted on the topic.

In partnership with my publisher Pearson, the "A.I. Catalyst" conference was held earlier this month in the O'Reilly Media platform. It has now been cleaned up and released for anyone to view as a standalone three-hour video. In it, we cover the full Large Language Model (LLM) lifecycle from development to deployment.

The presenters are at the absolute vanguard on their topics:
• Sinan Ozdemir: The A.I. entrepreneur and author introduces the theory behind Transformer Architectures and LLMs like BERT, GPT, and T5.
• Melanie Subbiah: A first author on the original GPT-3 paper, Melanie leads interactive demos of the broad range of LLM capabilities.
• Shaan Khosla: A data scientist on my team at Nebula.io, he details practical tips on training, validating, and productionizing LLMs.

If you don't have access to the O'Reilly online platform through your employer or school, you can use my special code "SDSPOD23" to get a 30-day trial and enjoy the video for free!

Check it out here: learning.oreilly.com/videos/catalyst-conference-nlp/9780138224912/

NLP with ChatGPT (and other LLMs)

Over 1400 people registered for yesterday's "NLP with ChatGPT (and other LLMs)" conference that I hosted in the O'Reilly Media platform. Kudos to speakers Sinan, Melanie and Shaan for making it a smashing success 🎉

This screenshot is a taste of what it looked like from inside the broadcasting platform, captained flawlessly by producers Joan Baker and Nurul Ishak, PMP.

The presenters each spent 30 minutes presenting on their topics and then engaged in riveting Q&A with the highly engaged attendees:
• Sinan Ozdemir: The A.I. entrepreneur and author introduced the theory behind Transformer Architectures and LLMs like BERT, T5, and GPT.
• Melanie Subbiah: A first author on the original GPT-3 paper, she led interactive demos of the broad range of capabilities of LLMs like ChatGPT.
• Shaan Khosla: A data scientist on my team at Nebula.io, he detailed practical tips on training, validating, and productionizing LLMs hands-on in Python.

I've heard word that, unusually for a live event in O'Reilly, the footage of this conference will be made available as a video within the platform. Stay tuned for details!

Data Engineering 101

Today's episode is all about Data Engineering — particularly the tools and techniques that Data Scientists should know. "Fundamentals of Data Engineering" book co-authors Matthew Housley and Joe Reis are guests!

Matt and Joe:
• Co-authored the brand-new "Fundamentals of Data Engineering" book that was published by O'Reilly Media and is already a bestseller.
• Co-founded the data architecture and data engineering consultancy Ternary Data. Joe is CEO of the firm while Matt is CTO.

In addition, Joe:
• Is an adjunct professor at the University of Utah.
• Previously founded several tech companies and has held both software engineering and data science roles.
• Holds a math degree from the University of Utah.

Matt:
• Holds a PhD in math from the University of Utah.
• Worked as a professor before becoming a data scientist in industry.

Today’s episode will appeal primarily to technical experts like data scientists and data engineers, but will also be of interest to anyone who manages technology projects that involve data flows.

In this episode, Matt and Joe detail:
• Why they identify as “recovering data scientists”.
• What kinds of people tend to become data scientists versus what kinds tend to become data engineers.
• Key components of their book such as latency trade-offs and the six data engineering undercurrents.
• Their favorite data engineering tools and techniques.
• What the Live Data Stack is and how it’s putting various data professional titles on a collision course.
• The biggest data engineering problems firms face and how to fix them.

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

Translations of Deep Learning Illustrated – Now available in Traditional Chinese

My book, Deep Learning Illustrated, recently became available in Traditional Chinese alongside the existing Russian, German, and Korean translations. The new edition instantly became a #1-bestseller in Taiwan.

Thanks to Neville Huang for diligently translating to Traditional Chinese from the original English. Neville also tipped me off to the #1-bestseller status — I've put the screenshot he shared with me on LinkedIn.

O'Reilly + JK October & November ML Foundations LIVE Classes open for registration

We're halfway through my live 14-class "ML Foundations" curriculum, which I'm offering via O'Reilly Media. The first Probability class is on Wednesday with five of the seven remaining classes open for registration now:

• Oct 6 — Intro to Probability
• Oct 13 — Probability II and Information Theory
• Oct 27 — Intro to Statistics
• Nov 3 — Statistics II: Regression and Bayesian
• Nov 17 — Intro to Data Structures and Algorithms

The final two classes will be in December and are on Computer Science topics: Hashing, Trees, Graphs, and Optimization. Registration for them should open soon.

All of the training dates and registration links are provided at jonkrohn.com/talks.

A detailed curriculum and all of the code for my ML Foundations series is available open-source in GitHub here.

O'Reilly + JK October ML Foundations LIVE Classes open for registration

The Linear Algebra classes of my "ML Foundations" curriculum, offered via the O'Reilly Media platform, are in the rear-view mirror. Two Calculus classes are coming up soon and the Probability classes just opened for registration:

• Sep 15 — Calculus III: Partial Derivatives
• Sep 22 — Calculus IV: Gradients and Integrals
• Oct 6 — Intro to Probability
• Oct 13 — Probability II and Information Theory
• Oct 27 — Intro to Statistics

Overall, four subject areas are covered:
• Linear Algebra (3/3 classes DONE)
• Calculus (2/4 classes DONE)
• Probability and Statistics (4 classes)
• Computer Science (3 classes)

Hope to see you in class! Sign up opens about two months prior to each class. All of the training dates and registration links are provided at jonkrohn.com/talks

A detailed curriculum and all of the code for my ML Foundations series is available open-source in GitHub here.

Tutorial on "Big O Notation"

Brand-new, hands-on intro to "Big O Notation" — an essential computer science concept. "Big O" allows us to weigh the compute-time vs memory-usage trade-offs of all algorithms, including machine learning models.

This YouTube video is a 45-minute, standalone excerpt from my six-hour "Data Structures, Algorithms, and ML Optimization" course, which focuses on code demos in Python to make understanding concepts intuitive, fun, and interactive.

If you have an O'Reilly Media subscription, the full course was recently published here.

If you'd like to purchase the course, Pearson is offering it this week (until August 28th) at a 70% discount as their "Video Deal of the Week". The URL for this unusually deep discount is here.

This "DSA and ML Optimization" course is the fourth and final quarter of my broader ML Foundations curriculum. All of the associated code is available open-source via GitHub.

Filming ""Data Structures, Algorithms, and Machine Learning Optimization"" LiveLessons

Silly times on set filming my "Data Structures, Algorithms, and Machine Learning Optimization" videos, which — over 6.5 interactive hours — introduce critical Computer Science concepts for ML and Data Science.

These videos were recently published in the O'Reilly Media platform.

These CS-focused videos are the fourth and final quarter of the subject areas covered in my broader "ML Foundations" curriculum — the first three being Linear Algebra, Calculus, and Probability. All of the code from the curriculum is available open-source in GitHub.

And my "Math for ML" playlist on O'Reilly captures all of the videos in this curriculum in one place.

Intro to Regression Models – O'Reilly Live Lessons

My new 80-minute intro to Regression Models is up on YouTube! It's packed with hands-on code demos in Python-based Jupyter notebooks to make learning regression intuitive, interactive, and maybe even fun :)

This lesson is an excerpt from my 9-hour "Probability and Statistics for Machine Learning" video tutorial, which is available via O'Reilly here.

All of the code is available open-source via GitHub.

Upcoming O'Reilly Calculus Classes

Starting a week today, I'm offering my entire "ML Foundations" curriculum as a series of 14 live, interactive workshops via O'Reilly Media. The first five classes are open for registration; two are already waitlist-only, so grab a spot now:

• Jul 14 — Intro to Linear Algebra (waitlisted)
• Jul 21 — LinAlg II: Matrix Tensors (5 spots remaining)
• Jul 28 — LinAlg III: Eigenvectors (waitlisted)
• Aug 12 — Intro to Calculus (143 spots remaining)
• Aug 18 — Calc II: AutoDiff (148 spots remaining)

REGARDING THE WAITLIST: I have a made a request with O'Reilly to increase the maximum class size from 600 students to 1000, so if you sign up for a waitlisted class now, you should still be able to get in.

Overall, there will be four subject areas covered:
• Linear Algebra (3 classes)
• Calculus (4 classes)
• Probability and Statistics (4 classes)
• Computer Science (3 classes)

Sign up opens about two months prior to each class. All 14 training dates, running from next week through December, are provided at jonkrohn.com/talks

A detailed curriculum and all of the code for my ML Foundations series is available open-source in GitHub here.