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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

Contrasting fastai with Keras

In this section, we'll cover some of the similarities and differences between fastai and Keras. While both frameworks provide high-level APIs for deep learning, there are some significant differences between them in terms of their architecture and approach to the problem, as well as differences between the communities using each. By contrasting these two frameworks, you will get a clearer idea of the strengths of fastai and be better prepared for the detailed examinations of fastai applications that are coming in subsequent chapters.

Getting ready

If you have used Keras recently, then you'll be in good shape to benefit from this section. If you haven't used Keras before, or it's been a while since you've used it, I recommend that you take a brief look at this tutorial so that you have a fresh overview of Keras: https://keras.io/getting_started/intro_to_keras_for_engineers/.

How to do it…

In this section, we will...

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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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