Daniel Bourke
VERIFIED TECHNICAL DOSSIERSources checked

Daniel Bourke

Machine Learning Engineer

2 Verified ArtifactsSource Checked & Attributed

Verified Proof of Work Artifacts

2 items cataloged

Each artifact below represents an authenticated research publication, production code repository, or technical architectural framework directly authored or co-created by Daniel Bourke. Every entry undergoes editorial source verification.

#1
EXPLANATION Checked Sep 22, 2026

Learn PyTorch for Deep Learning: Zero to Mastery

Authored and recorded the 25-hour open-access PyTorch curriculum covering tensors, neural networks, computer vision, transfer learning, and model deployment.

Model & Execution Context:PyTorch 2.x, TorchVision, CUDA, transfer learning with EfficientNet and ViT.
Scope & Limitations

Focuses on foundational vision and classification rather than distributed pre-training of 70B+ LLMs.

#2
IMPLEMENTATION Checked Sep 22, 2026

Nutrio: Food Vision AI Mobile Application Architecture

Created the open-source PyTorch textbook repository with 15k+ stars and engineered Nutrio, an offline edge computer vision app for automated dietary nutrition tracking.

Model & Execution Context:CoreML, MobileNetV3, iOS Swift integration, PyTorch quantization.
Scope & Limitations

Edge model accuracy is constrained by real-world mixed-plate food segmentation ambiguity.