Jeremy Howard
AI Infrastructure Architect
Verified Proof of Work Artifacts
2 items catalogedEach artifact below represents an authenticated research publication, production code repository, or technical architectural framework directly authored or co-created by Jeremy Howard. Every entry undergoes editorial source verification.
Fastai Deep Learning Framework and Layered API
A layered high-level and low-level deep learning library built on top of PyTorch that drastically democratizes state-of-the-art computer vision, NLP, and tabular models with decoupled optimizer and scheduler primitives.
Designed primarily for PyTorch ecosystem; high-level abstractions require delving into mid-level API for highly custom multi-modal tensor graphs.
ULMFiT: Universal Language Model Fine-tuning for Text Classification
Seminal research paper introducing inductive transfer learning to natural language processing via 3-step pre-training, discriminative fine-tuning, and slanted triangular learning rates, outperforming prior custom architectures by orders of magnitude.
Evaluated on recurrent AWD-LSTM architectures prior to pervasive multi-head self-attention scale; inductive bias differs from current masked and autoregressive transformers.