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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

Setting up a fastai environment in Google Colab

If you are already familiar with the Google Colab environment or want to take advantage of Google's overall machine learning ecosystem, Colab may be the right environment for you to use to explore fastai. In this section, we'll go through the steps to get set up with Colab and validate that it's ready for you to use with fastai.

Getting ready

To use Colab, you will need a Google ID and access to Google Drive. If you don't already have a Google ID, follow the instructions here to create one: https://support.google.com/accounts/answer/27441?hl=en.

Once you have a Google ID, you need to confirm that you have access to Google Drive. You need access to Drive because it acts as the storage system for Colab. You save your notebooks and data in Drive when you are working in Colab. Follow the instructions here to get access to Drive: https://support.google.com/drive/answer/2424384?co=GENIE.Platform%3DDesktop&hl...

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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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