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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
Languages
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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

Test your knowledge

Now that you have completed the recipes in this chapter, follow the steps shown here to exercise what you have learned. You will do this by adapting one of the notebooks you worked through in this chapter so that it works with a new dataset.

Getting ready

Follow these steps to upload a new tabular dataset:

  1. Go to the site for the Kaggle competition on future sales prediction (https://www.kaggle.com/c/competitive-data-science-predict-future-sales/data) and accept the conditions for the competition to get access to the datasets associated with the competition.
  2. Download the sales_train.csv.zip and test.csv.zip files.
  3. Unzip the downloaded files to extract sales_train.csv and test.csv.
  4. From the Terminal in your Gradient environment, make your current directory /storage/archive:
    cd /storage/archive
  5. Create a folder called /storage/archive/price_prediction:
    mkdir price_prediction
  6. Upload sales_train.csv and test.csv to /storage/archive/price_prediction...
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