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