Practical Deep Learning for Coders
by fast.ai
Jeremy Howard's top-down deep learning course: a working model in lesson one, the maths afterwards.
Overview
Practical Deep Learning for Coders is fast.ai's free flagship course, taught by Jeremy Howard, and it is the best-known example of top-down teaching in machine learning: lesson 1 trains a working image classifier in a handful of lines before any theory arrives, and the mathematics is filled in later once you have something running. Part 1 is nine lessons of roughly 90 minutes each — 1 Getting started, 2 Deployment, 3 Neural net foundations, 4 Natural Language (NLP), 5 From-scratch model, 6 Random forests, 7 Collaborative filtering, 8 Convolutions (CNNs), plus a bonus Data ethics lesson — covering computer vision, NLP via Hugging Face Transformers, tabular analysis, recommender systems, and shipping a trained model as a Gradio app. Part 2 continues with 17 lessons numbered 9 to 25 that reimplement Stable Diffusion from scratch alongside a hand-built mini-framework called miniai, working through matrix multiplication, mean shift clustering, backpropagation, autoencoders, initialization and normalization, accelerated SGD and ResNets, DDPM and DDIM, mixed precision, super-resolution, attention and transformers, and latent diffusion — over 30 hours of video, and it states plainly that it expects a reasonably confident practitioner who has finished Part 1 or equivalent. The stack is PyTorch plus the fastai library in Jupyter notebooks, designed to run free on Kaggle or Paperspace so no local GPU is required. The companion book, Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD, runs 20 chapters and is free to read as executable notebooks at github.com/fastai/fastbook. The 2022 rewrite is the current edition; successive launches have front-paged Hacker News (683 points in 2016, 556 in 2019, 423 for the 2022 rewrite).
At a Glance
- Topic
- ML
- Level
- Beginner
- Format
- Course
- Cost
- Free
- Duration
- Part 1: 9 lessons x ~90 min (~14h video, typically 8-10 weeks); Part 2 adds 17 lessons and 30+ hours
- Provider
- fast.ai
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Train and fine-tune an image classifier in the first lesson, before any theory is introduced
- ✓Deploy a trained model as a working Gradio web application other people can use
- ✓Build a neural network and its training loop from scratch to see what fastai hides
- ✓Fine-tune a Hugging Face Transformers model for a real NLP classification task
- ✓Apply random forests and gradient boosting to tabular problems, and interpret the results
- ✓Build a collaborative-filtering recommender with embeddings and understand what those embeddings encode
- ✓Understand convolutions, pooling and CNN architectures well enough to modify them yourself
- ✓In Part 2, reimplement Stable Diffusion end to end including DDPM/DDIM sampling and latent diffusion
Highlights
- •Top-down teaching: a working model in lesson 1 and the maths afterwards, the inverse of most university courses
- •Genuinely free with no upsell — videos, notebooks and the full 20-chapter companion book are all open
- •No GPU required; every notebook is built to run on free Kaggle or Paperspace instances
- •Jeremy Howard's credibility is earned in competition rather than credentials (former Kaggle president and top-ranked competitor)
- •Part 2 is one of very few courses that rebuilds Stable Diffusion from first principles instead of calling an API
- •Repeatedly front-paged on Hacker News across four editions, peaking at 683 points with 91 comments
Who It’s For
Best For
- ✓Working software engineers moving into deep learning without a research background
- ✓Self-taught practitioners who learn by building rather than by reading proofs
- ✓Anyone who wants real intuition for what transfer learning and fine-tuning actually do
- ✓Kaggle competitors looking for a fast route to a competitive baseline
Prerequisites
- •About a year of coding experience, ideally in Python
- •High-school-level mathematics — no calculus or linear algebra course required
- •A free Kaggle or Paperspace account if you have no local GPU
- •For Part 2 only: Part 1 or equivalent confidence writing SGD training loops in PyTorch
FAQ
What is Practical Deep Learning for Coders?
fast.ai's free flagship course, aimed at working coders with about a year of programming experience and no research background. Part 1 covers computer vision, NLP, tabular data, collaborative filtering and deployment across nine 90-minute lessons; Part 2 rebuilds Stable Diffusion from scratch. By the end of Part 1 you can train, interpret and ship a deep learning model as a live web app, all on free cloud GPUs.
Is Practical Deep Learning for Coders free?
Practical Deep Learning for Coders is free to access.
What level is Practical Deep Learning for Coders for?
Practical Deep Learning for Coders is aimed at a beginner audience. Recommended background: About a year of coding experience, ideally in Python, High-school-level mathematics — no calculus or linear algebra course required, A free Kaggle or Paperspace account if you have no local GPU, For Part 2 only: Part 1 or equivalent confidence writing SGD training loops in PyTorch.
How long does Practical Deep Learning for Coders take?
Expect roughly Part 1: 9 lessons x ~90 min (~14h video, typically 8-10 weeks); Part 2 adds 17 lessons and 30+ hours. Most learners work through it at their own pace.
What will I learn from Practical Deep Learning for Coders?
You'll learn: Train and fine-tune an image classifier in the first lesson, before any theory is introduced; Deploy a trained model as a working Gradio web application other people can use; Build a neural network and its training loop from scratch to see what fastai hides; Fine-tune a Hugging Face Transformers model for a real NLP classification task; Apply random forests and gradient boosting to tabular problems, and interpret the results; Build a collaborative-filtering recommender with embeddings and understand what those embeddings encode; Understand convolutions, pooling and CNN architectures well enough to modify them yourself; In Part 2, reimplement Stable Diffusion end to end including DDPM/DDIM sampling and latent diffusion.
Topics
Sources
This page was written from 4 sources, 2 on domains other than course.fast.ai.