If you want to contribute to fastai , be sure to review the contribution guidelines. This project adheres to fastai's code of conduct. By participating, you are expected to uphold this code. We use GitHub issues for tracking requests and bugs, so please see fastai forum for general questions and discussion. The fastai project strives to abide by generally accepted best practices in open-source software development:.
A detailed history of changes can be found here. Copyright onwards, fast. Licensed under the Apache License, Version 2. Skip to content. Star Branches Tags. Could not load branches. Could not load tags. Latest commit. NLP datasets. The dataset contains 30, training and 1, testing examples for each class. Image localization datasets.
Audio classification. Additional labels include name of individual making the vocalization and its age. Medical imaging datasets. This web site covers the book and the version of the course, which are designed to work closely together. If you haven't yet got the book, you can buy it here.
It's also freely available as interactive Jupyter Notebooks; read on to learn how to access them.. If you're ready to dive in right now, here's how to get started. If you want to know more about this course, read the next sections, and then come back here.
To watch the videos, click on the Lessons section in the navigation sidebar. The lessons all have searchable transcripts; click "Transcript Search" in the top right panel to search for a word or phrase, and then click it to jump straight to video at the time that appears in the transcript. Each video covers a chapter from the book. The entirety of every chapter of the book is available as an interactive Jupyter Notebook. Jupyter Notebook is the most popular tool for doing data science in Python, for good reason.
It is powerful, flexible, and easy to use. We think you will love it! Since the most important thing for learning deep learning is writing code and experimenting, it's important that you have a great platform for experimenting with code. To get started, we recommend using a Jupyter Server from one of the recommended online platforms click the links for instructions on how to use these for the course :.
It is built on top of a hierarchy of lower-level APIs which provide composable building blocks. This way, a user wanting to rewrite part of the high-level API or add particular behavior to suit their needs does not have to learn how to use the lowest level. It's very easy to migrate from plain PyTorch, Ignite, or any other PyTorch-based library, or even to use fastai in conjunction with other libraries.
Generally, you'll be able to use all your existing data processing code, but will be able to reduce the amount of code you require for training, and more easily take advantage of modern best practices. Here are migration guides from some popular libraries to help you on your way:. When installing with mamba or conda replace -c fastchan in the installation with -c pytorch -c nvidia -c fastai , since fastchan is not currently supported on Windows. This makes tasks such as computer vision in Jupyter on Windows many times slower than on Linux.
This limitation doesn't exist if you use fastai from a script. After that model can now be saved using the save method. You can download the code and the dataset using this ,, link. If you have questions or comments, then please put them in the comments section below. So if you like this blog post, please like it and subscribe to our data spoof community to get real-time updates. You can follow our Facebook page to get notifications whenever we upload any post so you can never miss any update from us.
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