Deep Learning with fastai Cookbook

Mark Ryan

ISBN 10: 1800208103 / ISBN 13: 9781800208100

Editore: Packt Publishing Limited, GB, 2021

Riguardo questo articolo

Harness the power of the easy-to-use, high-performance fastai framework to rapidly create complete deep learning solutions with few lines of codeKey FeaturesDiscover how to apply state-of-the-art deep learning techniques to real-world problemsBuild and train neural networks using the power and flexibility of the fastai frameworkUse deep learning to tackle problems such as image classification and text classificationBook Descriptionfastai 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 learnPrepare real-world raw datasets to train fastai deep learning modelsTrain fastai deep learning models using text and tabular dataCreate recommender systems with fastaiFind out how to assess whether fastai is a good fit for a given problemDeploy fastai deep learning models in web applicationsTrain fastai deep learning models for image classificationWho this book is forThis 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.

Codice articolo LU-9781800208100

Dati bibliografici

• Titolo: Deep Learning with fastai Cookbook
• Editore: Packt Publishing Limited, GB
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 1800208103
• ISBN 13: 9781800208100
• Rilegatura: Paperback
• Condizione: New
• Dimensioni: 23,49 L x 19,05 A x 1,81 P centimetri 23,49 centimetri di larghezza per 19,05 centimetri di altezza per 1,81 centimetri di profondità

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

Mark Ryan is a machine learning practitioner and technology manager who is passionate about delivering end-to-end deep learning applications that solve real-world problems. Mark has worked on deep learning projects that incorporate a variety of related technologies, including Rasa chatbots, web applications, and messenger platforms. As a strong believer in democratizing technology, Mark advocates for Keras and fastai as accessible frameworks that open up deep learning to non-specialists. Mark has a degree in computer science from the University of Waterloo and a Master of Science degree in computer science from the University of Toronto.