Deep Learning With Fast Ai
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Deep Learning for Coders with fastai and PyTorch
Author: Jeremy Howard
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2020-06-29
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Deep Learning with fastai Cookbook
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 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 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 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 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.
Deep Learning with Fast.ai
"Deep Learning with Fast.ai" "Deep Learning with Fast.ai" provides a comprehensive and contemporary roadmap for mastering deep learning through the lens of the Fast.ai ecosystem. The book opens by expertly blending the history, principles, and philosophy of modern neural networks with Fast.ai's distinctive top-down, practical teaching methodology, and design. Readers are introduced to the powerful abstractions and extensibility of Fast.ai, which leverages PyTorch for a seamless, high-performance user experience. Through clear explanations of core concepts—ranging from reproducibility and responsible AI to balancing mathematical theory with hands-on application—the book sets a strong foundation for learners and professionals alike. The book delves deeply into real-world workflows, guiding practitioners through flexible data pipelines, rigorous data augmentation, and innovative semi-supervised and out-of-core processing, all while addressing the challenges of diverse data sources. Subsequent chapters thoroughly unpack modeling fundamentals, from the versatile Learner abstractions and event-driven callbacks to advanced optimization, regularization, and efficient resource management. Covering transfer learning, model fine-tuning, and domain adaptation, the text empowers users to adapt state-of-the-art techniques for both typical and novel scenarios across computer vision, natural language processing, and tabular data—supplemented by practical chapters on model deployment, interpretation, and monitoring in production environments. Designed with the needs of modern machine learning practitioners and researchers in mind, "Deep Learning with Fast.ai" goes beyond standard use cases to explore innovative avenues such as integrating with external libraries, implementing custom neural components, and scaling for industrial hardware. With dedicated discussions on security, adversarial robustness, ethics, explainability, and the evolving future of AI, this book serves as both a practical toolkit and a forward-looking reference. Whether you are a developer, data scientist, researcher, or educator, this volume invites you to unlock the potential of deep learning with clarity, responsibility, and cutting-edge best practices.