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fast.ai Practical Deep Learning — Jeremy Howard Study Pack
Use this as a lecture map before watching, a review guide between lessons, or a timestamped index when you need to revisit one concept. Practical Deep Learning for Coders is taught by Jeremy Howard at fast.ai.
Interactive fast.ai Deep Learning study pack
Practical Deep Learning for Coders covers CNNs, NLP, tabular data, collaborative filtering, and the fast.ai library from the top down.
Convolutional neural networks for image classification using the fastai library and transfer learning from pretrained models.
Natural Language Processing covers text classification, language models, and building classifiers with ULMFiT.
Tabular data modeling with neural networks and embeddings for categorical variables, rivaling gradient boosting methods.
Collaborative filtering and recommendation systems using embeddings to represent users and items.
Data ethics, model interpretation, and the responsible deployment of deep learning systems.
What this lesson teaches
fast.ai's Practical Deep Learning for Coders is a hands-first course that teaches modern deep learning through implementation and experimentation before theory. Jeremy Howard teaches through the fastai library, enabling students to build state-of-the-art models for image classification, text analysis, tabular data, and recommendation systems with minimal code. The top-down approach means students see impressive results immediately, then progressively deepen their understanding.
Key concepts and takeaways
- Transfer learning with pretrained models (especially on ImageNet) dramatically reduces the data and compute needed for practical vision tasks.
- Discriminative learning rates — different rates for different layers — allow fine-tuning to preserve low-level features while adapting high-level ones.
- ULMFiT introduced the idea of fine-tuning a pretrained language model for downstream text classification tasks.
- Entity embeddings for categorical variables allow neural networks to outperform gradient boosting on many tabular tasks.
- Collaborative filtering learns latent factors for users and items; the dot product of a user vector and item vector predicts the rating.
- Data ethics includes bias detection, model transparency, and understanding the societal impact of deployed systems.
Review checklist
Build an image classifier using a pretrained ResNet and the fastai library; apply it to a dataset of your choice with transfer learning.
Fine-tune a language model on a text corpus, then use it to build a text classifier for a domain of interest.
Build a tabular model with entity embeddings for categorical features and compare it to a gradient boosting baseline.
Build a collaborative filtering recommender and analyze how learned embeddings reveal latent user preferences.
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