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Deep Learning with fastai Cookbook

You're reading from Deep Learning with fastai Cookbook Leverage the easy-to-use fastai framework to unlock the power of deep learning

Product type Paperback

Published in Sep 2021

Publisher Packt

ISBN-13 9781800208100

Length 340 pages

Edition 1st Edition

Languages

Python

Tools

fastai

Concepts

Deep Learning

Author (1):

Mark Ryan

View More author details

Table of Contents (10) Chapters

Preface

1. Chapter 1: Getting Started with fastai

2. Chapter 2: Exploring and Cleaning Up Data with fastai FREE CHAPTER

3. Chapter 3: Training Models with Tabular Data

4. Chapter 4: Training Models with Text Data

5. Chapter 5: Training Recommender Systems

6. Chapter 6: Training Models with Visual Data

7. Chapter 7: Deployment and Model Maintenance

8. Chapter 8: Extended fastai and Deployment Features

9. Other Books You May Enjoy

Conclusion and additional resources on fastai

In this book, you have explored a broad range of the capabilities of the fastai framework. By adapting the recipes in this book, you should be able to apply fastai to create deep learning models to make predictions on a wide variety of datasets. You will also be able to deploy your models in simple web applications.

There are many more capabilities in fastai beyond those covered in this book. Here are some additional fastai resources that you can use to learn more about the platform:

To dig deeper into fastai, you can check out the online documentation for the framework (https://docs.fast.ai/).
If you want a comprehensive guide to fastai, I highly recommend the outstanding book from Jeremy Howard (the originator of fastai) and Sylvain Gugger Deep Learning for Coders with Fastai and PyTorch (https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527).
Jeremy Howard's YouTube channel (https://www.youtube...

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Authors (1)

Ryan

Ryan

Mark Ryan is a machine learning practitioner and technology manager who is passionate about delivering end-to-end deep learning applications that solve real-world problems. Mark has worked on deep learning projects that incorporate a variety of related technologies, including Rasa chatbots, web applications, and messenger platforms. As a strong believer in democratizing technology, Mark advocates for Keras and fastai as accessible frameworks that open up deep learning to non-specialists. Mark has a degree in computer science from the University of Waterloo and a Master of Science degree in computer science from the University of Toronto.

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