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Fast.ai — Practical Deep Learning

Free

Practical deep learning for coders, from zero to deployment

FreeFree tier
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Type
Open Source
Company
fast.ai
Visit Website GitHub (40 stars)

About Fast.ai — Practical Deep Learning

Fast.ai's Practical Deep Learning for Coders is a free, comprehensive course designed for people with some coding experience who want to learn how to apply deep learning and machine learning to practical problems. The 2022 edition (Part 1), recorded at the University of Queensland, consists of 9 lessons of approximately 90 minutes each. It covers building and training deep learning models for computer vision, natural language processing, tabular analysis, and collaborative filtering, as well as creating random forests and regression models and deploying models. The course uses PyTorch, fastai, and Hugging Face, and is based on a free online book. No special hardware or university-level math is required—the course teaches necessary calculus and linear algebra. Over 6 million views and alumni at companies like Google Brain, OpenAI, and Amazon attest to its effectiveness. Taught by Jeremy Howard, former President and Chief Scientist of Kaggle.

Key Features

Build and train deep learning models for computer vision, NLP, tabular analysis, and collaborative filtering
Create random forests and regression models
Deploy models to production
Use PyTorch, fastai, and Hugging Face libraries
Free online book companion with 5-star rating
No specialized hardware or software required – uses free cloud resources
9 lessons, each about 90 minutes long
Taught by Jeremy Howard, former President and Chief Scientist of Kaggle
Covers practical deployment and includes a real project by lesson 2

Pros & Cons

Pros
  • Completely free course with high-quality video and book
  • No prior math or deep learning knowledge required – teaches what you need
  • Hands-on, practical approach: build and deploy a model by lesson 2
  • Taught by a world-renowned expert with industry and competition success
  • Large community and forum support; over 6 million views
  • Alumni have landed jobs at top tech companies and published research
  • Uses popular, industry-standard frameworks (PyTorch, fastai, Hugging Face)
Cons
  • Requires some coding experience (not for complete beginners)
  • Course is time-intensive (9 lessons of 90 minutes each plus projects)
  • Part 1 focuses on practical usage; deeper theory may be in Part 2
  • Assumes students can dedicate significant time to practice

Best For

Computer vision (e.g., image classification, object detection)Natural language processing (e.g., text classification, sentiment analysis)Tabular data analysis (e.g., regression, classification on structured data)Collaborative filtering (e.g., recommendation systems)Building and deploying end-to-end deep learning applications

FAQ

Is the course really free?
Yes, the entire course (Practical Deep Learning for Coders) is free, including all video lessons, the online book, and related resources.
Do I need special hardware or software?
No, the course shows you how to use free resources for both building and deploying models. No special hardware is required.
What math knowledge do I need?
None beyond basic programming math. The course teaches the needed calculus and linear algebra during the lessons.
What will I learn in this course?
You will learn to build and train deep learning models for computer vision, natural language processing, tabular analysis, and collaborative filtering, as well as random forests, regression, and model deployment.
Who is the instructor?
The course is taught by Jeremy Howard, who leads the development of fastai, was the top-ranked Kaggle competitor for two years, and previously served as President and Chief Scientist of Kaggle.