Machine Learning Foundations

I’m very excited to announce the beginning of a new journey called the Machine Learning Foundations series. As discussed in the video announcement above, this series will initially consist of eight 3.5-hour classes offered within the O’Reilly online learning platform from late May through early September:

1. May 28th — Intro to Linear Algebra

2. June 4th — Linear Algebra II: Matrix Operations

3. June 11th — Calculus I: Limits & Derivatives

4. June 25th —Calculus II: Partial Derivatives & Integrals

5. July 8th —Probability & Information Theory

6. July 23rd —Intro to Statistics

7. August 12th —Algorithms & Data Structures

8. early September — Optimization

Each class will feature:

• Rich, full-color illustrations

• Hands-on code demos in Python

• Fully worked-through pencil-and-paper questions and solutions

There are 605 seats available in each class. At the time of posting:

• The first two classes (on algebra) have fewer than ten seats each

• The second pair of classes (on calculus) have fewer than a hundred

• Registration for the fifth and sixth class is open and seats are filling up quickly.

As I have bandwidth, I will be publishing all of this content as free YouTube video tutorials, so if you’ve missed the classes, don’t worry! I will also probably be offering the classes again at some point, and eventually all of the content will be brought together neatly as a book.

You can read more about the Machine Learning Foundations series — including a detailed syllabus for each class and the developing body of open-source code — in GitHub here.

ODSC East Virtual Conference: So much fun, I couldn't resist singing

After my 3.5-hour technical lecture on Wednesday morning, I rewarded my 300 attendees’ remarkable attention spans with a cover of Willin’ by Little Feat :)

It was impromptu but well-received so maybe I should start doing this regularly at data science conferences…?

Video Tutorial on the Central Limit Theorem

Brand-new video tutorial: an intro to the Central Limit Theorem, a whacky phenomenon that underlies (almost) all modern statistics and machine learning. My hands-on code demos (in Python) make it easy to grasp why the whackiness happens.

Could be the perfect thing for ya if you're avoiding the plague by working from home and/or you're looking for something new to learn!

Announcing A4N: The Artificial Neural Network News Network →

It’s my great pleasure to announce A4N — the Artificial Neural Network News Network — a new podcast series. Our show is a lighthearted vehicle for discussion of the latest developments in A.I., machine learning, and data science, in which we both introduce technical aspects of these advances as well as their social implications.

The intended audience is anyone interested in automation, A.I., or the future, with brief sections catering especially to professionals working in the fields of data science or software engineering.

The show is hosted by Andrew Vlahutin, Grant Beyleveld, Vince Petaccio II, and myself — data scientists and machine learning engineers from untapt, an A.I. company. The podcast is commercial-free and we are bent on becoming your favorite — and most entertaining — source for A.I.-related news.

Episodes are hosted on jonkrohn.com (here) and are syndicated across all of the most popular platforms: Apple Podcasts, Spotify Podcasts, and Google Podcasts.

As with the Joe Rogan Experience and many other beloved podcasts, we also provide a raw video feed of the podcast recording session, which is available on YouTube.

Click through to the Medium post provided here for all of the links and more details :)

Tutorial, Booksigning, and "Ask the Expert" Session at ODSC East in Boston in April →

On April 15th in Boston, I’ll be giving a hands-on, 3.5-hour introduction to deep learning — featuring the brand-new TensorFlow 2 library — at the Open Data Science Conference East. In addition, I’ll be giving away dozens of free copies of my book Deep Learning Illustrated during a book-signing session, as well as answering any questions you might have during an “Ask the Expert” session.

First (of Many) Free Video Tutorials

After years of producing popular data-science tutorials that exist behind paywalls, I'm delighted to announce my first (of many) 100% free videos. This first one is on "Big O Notation" -- a critical computing concept.

Deep Learning Study Group XVII: Capsule Nets, BERT & Book Launch →

For this session, Dmitri Nesterenko and Grant Beyleveld presented on Capsule Networks (slides here) and "BERT & Friends" (slides here), respectively.

This session also served as a book launch for my, Grant Beyleveld, and Aglae Bassens' book, Deep Learning Illustrated, the content of which was influenced in large part by the previous sessions of the Deep Learning Study Group. The first photo of the three authors together with their book is provided above.

Click through for all of the session details, including the recommended preparatory work and the topics we’re considering for our next session.

Open Data Science Conference West

My first experience at ODSC West exceeded my lofty expectations. Personal highlights include:

• Teaching an oversubscribed four-hour tutorial on deep learning with TensorFlow (pictured above; slides here)

• Meeting hundreds of wonderful fans at my Deep Learning Illustrated book-signing (pictured below)

• Sharing meals with some of the biggest names in data science, including Pieter Abbeel, Kirk Borne, and Kate Strachnyi

Conversation on "Artificial Intelligence and the Brain" with Dr. Heather Berlin

Last night in New York, neuroscientist Heather Berlin and I gave a talk on artificial intelligence and the brain at a Magdalen College alumni event. We’re pictured here with charming host, Sir David Clary.

I had a lot of fun, found Heather’s content fascinating, and loved the thorough audience questions.

Wrapped Filming in San Fran

In San Francisco, having wrapped filming for a series of interactive videos using TensorFlow 2.0 and Keras for Natural Language Processing, Machine Vision, and Deep Reinforcement Learning.

All of the code is available for free now in GitHub.

"Data Science at Home" Podcast →

Another day, another podcast release!

This one is with the charismatic Francesco Gadaleta on his "Data Science at Home" podcast. We talked about:

• How to deal with bias in machine learning used to match jobs to candidates

• Guidelines to take into account whenever you implement a deep learning model

• The future of AI

Deep Learning "Spoken" on Data Science Imposters' Podcast →

My appearance on the Data Science Imposters podcast went live today. The co-hosts, Jordy and Antonio, came to untapt HQ to discuss a wildly broad range of topics that included untapt and my book, Deep Learning Illustrated. Having such warm co-hosts, as well as being able to record in-person, ensured that we created an especially fun podcast.

My Conversation on DataBytes podcast released!

Had so much fun chatting with Susan Wang about deep learning on her DataBytes podcast.

My Interview on Podcast.__init__ Released Today →

Enjoyed the deeply thoughtful questions from Tobias Macey on his Python podcast, Podcast.__init__, released today. We discussed what deep learning is, how we deploy it to solve human-resources problems at untapt, and my newly-released book Deep Learning Illustrated.

First In-Store Spotting of Deep Learning Illustrated

I spotted Deep Learning Illustrated for the first time in-store at the Barnes & Noble location on New York’s Upper West Side.

"TensorFlow 2.0 vs PyTorch": Popular Talk at Metis Last Week

Had a blast speaking to a packed, inquisitive house at Metis New York last week about deep learning (specifically, contrasting the TensorFlow 2.0 and PyTorch libraries) as well as signing copies of my newly-released Deep Learning Illustrated book.

Thanks to Nathan Vermeiren, Jennifer Raimone, Jane Durand, and Dun Sattler for organizing and hosting the popular event, which had a long waiting list and several hundred folks join via live stream.

Given the popularity of this topic, I’ll be sure to give this talk again soon!

10% off my 30-Hour Deep Learning Course

It's the final chance to sign up for my comprehensive, 30-hour Deep Learning course, which kicks off on Sat Oct 19 at the NYC Data Science Academy. Use the code DLT2019 to get 10% off!

The course features the brand-new TensorFlow 2.0 library, used in conjunction with Keras and PyTorch, to cover all of the underlying Deep Learning theory and all of its modern applications. Optionally, as the course progresses, you can work with me directly to develop your own Deep Learning project from the ground up.

Here's a 30-second video of my popular course demo night last week, which also served as a book-launch / book-signing for my newly released Deep Learning Illustrated.

"Deep Learning Illustrated" is an Instant #1 Bestseller

My newly-released book Deep Learning Illustrated became an instant #1 Bestseller this week in two Amazon Kindle categories: Neural Networks and Data Mining.