Deep Learning with fastai Cookbook
- Paperback: 340 pages
- Publisher: WOW! eBook (September 24, 2021)
- Language: English
- ISBN-10: 1800208103
- ISBN-13: 978-1800208100
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
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 Deep Learning with fastai Cookbook book wraps up with an overview of the advanced features of fastai.
- 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
By the end of this Deep Learning with fastai Cookbook 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.
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