Deep Learning with fastai Cookbook: Leverage the easy-to-use fastai framework to unlock the power of deep learning

Length: 340 pages
Edition: 1
Language: English
Publisher: Packt Publishing
Publication Date: 2021-09-24
ISBN-10: 1800208103
ISBN-13: 9781800208100
Sales Rank: #2057926 (See Top 100 Books)

Harness the power of the easy-to-use, high-performance fastai framework to rapidly create complete deep learning solutions with few lines of code

Key Features

Discover how to apply state-of-the-art deep learning techniques to real-world problems
Build and train neural networks using the power and flexibility of the fastai framework
Use deep learning to tackle problems such as image classification and text classification

Book Description

fastai is an easy-to-use deep learning framework built on top of PyTorch that lets you rapidly create complete deep learning solutions with as few as 10 lines of code. Both predominant low-level deep learning frameworks, TensorFlow and PyTorch, require a lot of code, even for straightforward applications. In contrast, fastai handles the messy details for you and lets you focus on applying deep learning to actually solve problems.

The book begins by summarizing the value of fastai and showing you how to create a simple ‘hello world’ deep learning application with fastai. You’ll then learn how to use fastai for all four application areas that the framework explicitly supports: tabular data, text data (NLP), recommender systems, and vision data. As you advance, you’ll work through a series of practical examples that illustrate how to create real-world applications of each type. Next, you’ll learn how to deploy fastai models, including creating a simple web application that predicts what object is depicted in an image. The book wraps up with an overview of the advanced features of fastai.

By the end of this fastai book, you’ll be able to create your own deep learning applications using fastai. You’ll also have learned how to use fastai to prepare raw datasets, explore datasets, train deep learning models, and deploy trained models.

What you will learn

Prepare real-world raw datasets to train fastai deep learning models
Train fastai deep learning models using text and tabular data
Create recommender systems with fastai
Find out how to assess whether fastai is a good fit for a given problem
Deploy fastai deep learning models in web applications
Train fastai deep learning models for image classification

Who this book is for

This book is for data scientists, machine learning developers, and deep learning enthusiasts looking to explore the fastai framework using a recipe-based approach. Working knowledge of the Python programming language and machine learning basics is strongly recommended to get the most out of this deep learning book.

Getting Started with fastai
Exploring and Cleaning Up Data with fastai
Training Models with Tabular Data
Training Models with Text Data
Training Recommender Systems
Training Models with Visual Data
Deployment and Model Maintenance
Extended fastai and Deployment Features

Deep Learning with fastai Cookbook
Contributors
About the author
About the reviewer
Preface
    Who this book is for
    What this book covers
    To get the most out of this book
    Download the example code files
    Download the color images
    Conventions used
    Sections
    Getting ready
    How to do it…
    How it works…
    There's more…
    See also
    Get in touch
    Share Your Thoughts
Chapter 1: Getting Started with fastai
    Technical requirements
    Setting up a fastai environment in Paperspace Gradient
        Getting ready
        How to do it…
        How it works…
        There's more…
    Setting up a fastai environment in Google Colab
        Getting ready
        How to do it…
        How it works…
        There's more…
    Setting up JupyterLab environment in Gradient
        Getting ready
        How to do it…
        How it works…
        There's more…
    "Hello world" for fastai – creating a model for MNIST
        Getting ready…
        How to do it…
        How it works…
        There's more…
    Understanding the world in four applications: tables, text, recommender systems, and images
        Getting ready
        How to do it…
        How it works…
    Working with PyTorch tensors
        Getting ready
        How to do it…
        How it works…
        There's more…
    Contrasting fastai with Keras
        Getting ready
        How to do it…
        How it works…
    Test your knowledge
Chapter 2: Exploring and Cleaning Up Data with fastai
    Technical requirements
    Getting the complete set of oven-ready fastai datasets
        Getting ready
        How to do it…
        How it works…
        There's more…
    Examining tabular datasets with fastai
        Getting ready
        How to do it…
        How it works…
        There's more…
    Examining text datasets with fastai
        Getting ready
        How to do it…
        How it works…
    Examining image datasets with fastai
        Getting ready
        How to do it…
        How it works…
        There's more…
    Cleaning up raw datasets with fastai
        Getting ready
        How to do it…
        How it works…
Chapter 3: Training Models with Tabular Data
    Technical requirements
    Training a model in fastai with a curated tabular dataset
        Getting ready
        How to do it…
        How it works…
    Training a model in fastai with a non-curated tabular dataset
        Getting ready
        How to do it…
        How it works…
    Training a model with a standalone dataset
        Getting ready
        How to do it…
        How it works…
    Assessing whether a tabular dataset is a good candidate for fastai
        Getting ready
        How to do it…
        How it works…
    Saving a trained tabular model
        Getting ready
        How to do it…
        How it works…
    Test your knowledge
        Getting ready
Chapter 4: Training Models with Text Data
    Technical requirements
    Training a deep learning language model with a curated IMDb text dataset
        Getting ready
        How to do it…
        How it works…
        There's more…
    Training a deep learning classification model with a curated text dataset
        Getting ready
        How to do it…
        How it works…
        There's more…
    Training a deep learning language model with a standalone text dataset
        Getting ready
        How to do it…
        How it works…
    Training a deep learning text classifier with a standalone text dataset
        Getting ready
        How to do it…
        How it works…
    Test your knowledge
        Getting ready
        How to do it…
Chapter 5: Training Recommender Systems
    Technical requirements
    Training a recommender system on a small curated dataset
        Getting ready
        How to do it…
        How it works…
    Training a recommender system on a large curated dataset
        Getting ready
        How to do it…
        How it works…
    Training a recommender system on a standalone dataset
        Getting ready
        How to do it…
        How it works…
    Test your knowledge
        Getting ready
        How to do it…
Chapter 6: Training Models with Visual Data
    Technical requirements
    Training a classification model with a simple curated vision dataset
        Getting ready
        How to do it…
        How it works…
    Exploring a curated image location dataset
        Getting ready
        How to do it…
        How it works…
    Training a classification model with a standalone vision dataset
        Getting ready
        How to do it…
        How it works…
    Training a multi-image classification model with a curated vision dataset
        Getting ready
        How to do it…
        How it works…
    Test your knowledge
        Getting ready
        How to do it…
Chapter 7: Deployment and Model Maintenance
    Technical requirements
    Setting up fastai on your local system
        Getting ready
        How to do it…
        How it works…
    Deploying a fastai model trained on a tabular dataset 
        Getting ready
        How to do it…
        How it works…
        There's more…
    Deploying a fastai model trained on an image dataset 
        Getting ready
        How to do it…
        How it works…
        There's more…
    Maintaining your fastai model
        Getting ready
        How to do it…
        How it works…
        There's more…
    Test your knowledge
        Getting ready
        How to do it…
Chapter 8: Extended fastai and Deployment Features
    Technical requirements
    Getting more details about models trained with tabular data
        Getting ready
        How to do it…
        How it works…
    Getting more details about image classification models
        Getting ready
        How to do it…
        How it works…
    Training a model with augmented data
        Getting ready
        How to do it…
        How it works…
    Using callbacks to get the most out of your training cycle
        Getting ready
        How to do it…
        How it works…
    Making your model deployments available to others
        Getting ready
        How to do it…
        How it works…
    Displaying thumbnails in your image classification model deployment
        Getting ready
        How to do it…
        How it works…
    Test your knowledge
        Explore the value of repeatable results
        Displaying multiple thumbnails in your image classification model deployment
    Conclusion and additional resources on fastai
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