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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

Working with PyTorch tensors

Throughout most of this book, the focus will be on the features provided by the fastai framework. However, some of the solutions that we'll review also exploit general Python libraries (such as the pandas library for deep learning applications with tabular data) as well as aspects of PyTorch, the low-level deep learning framework upon which fastai is built. To give you a small taste of PyTorch, in this section we'll go through some basic examples of using tensors, the PyTorch structure for multidimensional matrices.

Getting ready

If you are already familiar with NumPy arrays, then you will have a good basis for examining PyTorch tensors because tensors play much the same role for PyTorch as NumPy arrays do for general-purpose Python applications. If you are not familiar with NumPy arrays or it's been a while since you have had a chance to use them, take a bit of time to review them—for example, by going through this tutorial...

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