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What is fastbook?

bobymicroby/fastbook — explained in plain English

Analysis updated 2026-07-14 · repo last pushed 2022-12-11

Jupyter NotebookAudience · vibe coderComplexity · 2/5DormantLicenseSetup · easy

In one sentence

An interactive deep learning textbook teaching you to build working AI applications using fastai and PyTorch, with code you can run directly in your browser.

Mindmap

mindmap
  root((repo))
    What it does
      Interactive deep learning textbook
      Run code in browser
      Learn by doing
    Topics covered
      Train AI models
      Production and ethics
      Text and image analysis
    Tech stack
      Fastai library
      PyTorch framework
      Jupyter Notebooks
    Audience
      Programmers and beginners
      Founders and product managers
      Global multilingual readers
    Licensing
      Open-source code
      Copyrighted prose
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Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

What do people build with it?

USE CASE 1

Learn to train an AI model to recognize different breeds of pets.

USE CASE 2

Build a recommendation system using deep learning.

USE CASE 3

Analyze natural language text with AI models.

USE CASE 4

Learn how to put trained AI models into production.

What is it built with?

Jupyter NotebookFastaiPyTorchPythonGoogle Colab

How does it compare?

bobymicroby/fastbookjamisriram/academic-rag-assistantjuice500ml/notebook
Stars0
LanguageJupyter NotebookJupyter NotebookJupyter Notebook
Last pushed2022-12-112024-12-20
MaintenanceDormantStale
Setup difficultyeasyeasymoderate
Complexity2/52/53/5
Audiencevibe coderdeveloperresearcher

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

No local installation needed, the authors recommend opening and running the notebooks directly in a web browser using Google Colab.

The computer code is open-source and free to modify, but the written text and explanations are strictly copyrighted and cannot be republished without permission.

So what is it?

This repository contains the complete interactive textbook for learning deep learning, written by Jeremy Howard and Sylvain Gugger. It is the foundation for a popular online course and a published book, "Deep Learning for Coders with Fastai and PyTorch." The materials teach you how to build artificial intelligence applications using two tools: fastai (a software library designed to make deep learning approachable) and PyTorch (a widely used framework for building AI models). The goal is to help people create working AI applications without needing an advanced math degree. The content is structured as interactive documents called notebooks. A notebook lets you read explanatory text and immediately run real computer code in the same view, which makes it easier to experiment and learn by doing. The chapters walk through practical projects, starting from the basics of training a model and moving into real-world topics like putting AI into production, understanding ethics, analyzing text, and working with image data. You do not even need to install special software on your own computer to get started, the authors recommend opening the chapters directly in your web browser using a free Google tool called Colab. This resource is designed for programmers and beginners who want to break into machine learning but are intimidated by its typical academic reputation. If you are a founder, product manager, or someone who already knows a little bit of coding and wants to build AI-powered features, these notebooks guide you through creating actual working models. For example, you learn how to train a system to recognize different breeds of pets, build recommendation systems, and analyze natural language. A notable aspect of this project is its licensing, which reflects the creators' desire to share knowledge freely while protecting their work. The underlying computer code is open-source, meaning anyone can use and modify it for their own projects. However, the written explanations and prose are strictly copyrighted, meaning you cannot republish, reformat, or commercially broadcast the text without permission. The repository is also available in multiple languages, including Spanish, Chinese, and Korean, to make the material accessible to a global audience.

Copy-paste prompts

Prompt 1
I am going through the fastbook deep learning textbook. Help me understand how fastai makes training a pet breed classification model simpler compared to using raw PyTorch.
Prompt 2
I want to run the fastbook notebooks in Google Colab. Walk me through the steps to open a chapter and start running the code without installing anything on my computer.
Prompt 3
I finished the fastbook chapter on recommendation systems. Show me how to adapt that code to build a movie recommendation model using my own CSV data.
Prompt 4
I am reading the fastbook chapter on deploying AI to production. What are the key ethical considerations I should keep in mind when putting my model online?

Frequently asked questions

What is fastbook?

An interactive deep learning textbook teaching you to build working AI applications using fastai and PyTorch, with code you can run directly in your browser.

What language is fastbook written in?

Mainly Jupyter Notebook. The stack also includes Jupyter Notebook, Fastai, PyTorch.

Is fastbook actively maintained?

Dormant — no commits in 2+ years (last push 2022-12-11).

What license does fastbook use?

The computer code is open-source and free to modify, but the written text and explanations are strictly copyrighted and cannot be republished without permission.

How hard is fastbook to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is fastbook for?

Mainly vibe coder.

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