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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

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781800208100
Length 340 pages
Edition 1st Edition
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Author (1):
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Mark Ryan Mark Ryan
Author Profile Icon Mark Ryan
Mark Ryan
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Table of Contents (10) Chapters Close

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

Chapter 3: Training Models with Tabular Data

In the previous chapter, we learned how to ingest various kinds of datasets using fastai and how to clean up datasets. In this chapter, we are going to get into the details of training a model with fastai using tabular data. Tabular data, which is data organized in rows and columns that you would find in a spreadsheet file or a database table, is critical to most businesses. The fastai framework acknowledges the importance of tabular data by providing a full suite of features to support deep learning applications based on tabular data.

To explore deep learning with tabular data in fastai, we will return to the ADULT_SAMPLE dataset, one of the datasets we examined in Chapter 2, Exploring and Cleaning Up Data with fastai. By using this dataset, we will train a deep learning model, while also learning about the TabularDataLoaders (used to define the training and test datasets) and tabular_learner (used to define and train the model) objects...

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