Jon Krohn
Co-Founder & CEO at The University of Auckland
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolJon Krohn is a founder, author, and AI practitioner with nine years of industry experience translating cutting-edge deep learning research into commercial products and popular education. As Co-Founder & CEO of Y Carrot and former Chief Data Scientist at Nebula, he has led end-to-end AI projects—from research and high-frequency trading models to production ML APIs and agentic systems—while advising enterprise deployments at Lightning AI. He is a bestselling author of Deep Learning Illustrated, a prolific educator (130k+ students across O'Reilly and Udemy and 8M podcast downloads annually), and a recognized content creator who also develops AI TV segments for general audiences. His open-source contributions include practical TensorFlow and PyTorch notebooks that demystify core neural network concepts and image-classification pipelines. Combining a PhD from Oxford in neuroscience with hands-on ML engineering and startup leadership, he blends rigorous research instincts with a talent for making complex AI accessible and deployable. An often-overlooked strength is his consistent focus on tooling and guardrails that make generative models safer and more product-ready.
9 years of coding experience
13 years of employment as a software developer
PhD, Neuroscience, PhD, Neuroscience at University of OxfordBachelor of Science, Bachelor of Science at Wilfrid Laurier University
German, French, English
Github Skills (9)
neural-network10
keras10
pytorch10
machine-learning10
deep-learning10
tensorflow10
python10
mnist10
image-classification10
Programming languages (2)
Github contributions (5)
the-deep-learners/TensorFlow-LiveLessons
Apr 2017 - Apr 2020
"Deep Learning with TensorFlow" LiveLessons
Role in this project:
Data Scientist / ML Engineer
Data Scientist / ML EngineerContributions:269 commits, 13 PRs, 234 pushes in 3 years
Contributions summary:Jon's commits focused on implementing and refining a deep neural network for image classification. The primary contribution involved integrating a TFLearn model to classify the MNIST dataset, showcasing expertise in model architecture and data preprocessing. Subsequent changes involved cleaning up the notebook and upgrading the code, indicating ongoing improvements and a focus on achieving more accurate classification of the images.
deep-learningkerastensorflowchainer
jonkrohn/DLTFpT
Aug 2019 - Dec 2022
Deep Learning with TensorFlow, Keras, and PyTorch
Role in this project:Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:175 commits, 2 PRs, 160 pushes in 3 years 5 months
Contributions summary:Jon contributed multiple test notebooks that implemented shallow neural networks. Specifically, they implemented shallow neural networks in both TensorFlow and PyTorch, demonstrating an understanding of fundamental deep learning concepts. They also created and documented example notebooks involving activation functions, loss functions, and deep learning models, showcasing their expertise in the field.
pytorchdeep-learningmachine-learningkeras-tensorflowtensorflow
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