Lilly Tech Systems
Learn FastAI
Build world-class deep learning models in just a few lines of code. FastAI provides high-level components for vision, text, and tabular data — making deep learning accessible to everyone while remaining deeply customizable for experts.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
The fast.ai philosophy, top-down learning approach, built on PyTorch, and Jeremy Howard's vision.
2. Installation
Install fastai with pip, configure your environment, and set up GPU support.
3. Vision
Image classification with vision_learner, fine_tune, data augmentation, and model interpretation.
4. Tabular Data
TabularDataLoaders, categorical and continuous variables, and feature engineering.
5. NLP
Text classification, language model fine-tuning, and the ULMFiT approach.
6. Best Practices
Learning rate finder, mixed precision, callbacks, and deploying FastAI models.
What You'll Learn
By the end of this course, you'll be able to:
Classify Images
Train state-of-the-art image classifiers in under 10 lines of code using transfer learning.
Analyze Tabular Data
Build deep learning models for structured/tabular data that compete with gradient boosting.
Process Text
Fine-tune language models for text classification using the ULMFiT methodology.
Deploy Models
Export trained models and deploy them as web applications using FastAI's export tools.