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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

Technical requirements

Ensure that you have completed the setup sections from Chapter 1, Getting Started with fastai, and have a working Gradient instance or Colab setup. The recipes described in this chapter assume that you are using Gradient. Ensure that you have cloned the repository for the book from https://github.com/PacktPublishing/Deep-Learning-with-fastai-Cookbook and have access to the ch4 folder. This folder contains the code samples described in this chapter.

Some of the examples in this chapter will take over an hour to run.

Note

Do not use Colab for these examples. With Colab you cannot control the GPU that you get for a session and these examples may run for many hours. For the examples in this chapter, use a for-pay GPU-enabled Gradient environment to ensure they complete in a reasonable time.

The rest of the chapter is locked

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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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