Jeremy Howard
AI Infrastructure Architect
Co-founder of fast.ai and CEO of Answer.ai. Former President and Chief Scientist at Kaggle. Co-developed ULMFiT (Universal Language Model Fine-tuning), which established modern transfer learning in NLP before BERT and GPT. Author of 'Deep Learning for Coders with fastai and PyTorch'. Currently building GPU-accelerated agentic development environments and open foundation tools.
Areas of focus
Professional niches
Proof of Work
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.
Context: AWD-LSTM 3-layer language model pre-trained on WikiText-103; foundation for modern BERT/transformer transfer learning pipelines.
View missionFastai 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.
Context: PyTorch 2.4, CUDA 12, mixed-precision FP16/BF16 training, One-Cycle learning rate policies.
View mission