Deep Learning with fastai Cookbook
Mark Ryan
- 338 pagine
- English
- ePUB (disponibile sull'app)
- Disponibile su iOS e Android
Deep Learning with fastai Cookbook
Mark Ryan
Informazioni sul libro
Harness the power of the easy-to-use, high-performance fastai framework to rapidly create complete deep learning solutions with few lines of codeKey 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 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 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 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.
Domande frequenti
Informazioni
Chapter 1: Getting Started with fastai
- Setting up a fastai environment in Paperspace Gradient
- Setting up a fastai environment in Google Colaboratory (Google Colab)
- Setting up JupyterLab environment in Paperspace Gradient
- "Hello world" for fastai—creating a model for the Modified National Institute of Science and Technology (MNIST) dataset
- Understanding the world in four applications: tables, text, recommender systems, and images
- Working with PyTorch tensors
- Contrasting fastai with Keras
- Test your knowledge
Technical requirements
- Paperspace Gradient: https://gradient.paperspace.com/
- Google Colab: https://colab.research.google.com/notebooks/intro.ipynb
- Google Drive: https://drive.google.com
- Keras: https://keras.io/
Setting up a fastai environment in Paperspace Gradient
Getting ready
How to do it…
- Go to the Paperspace site and sign in using the account you created in the Getting ready section.
- From the pulldown at the top of the page, select Gradient:
- Select the Notebooks tab:
- Select the CREATE button.
- Enter a name for your notebook in the Name field.
- In the Select a runtime section, select fastai.
- In the Select a machine section, select Free-GPU or Free-P5000. Note that you may receive a message indicating out of capacity for the machine type you selected. If this happens, you can either choose another GPU-enabled machine type or wait a few minutes and try again with your original machine type. Also note that after your notebook is created, you can change the machine type—for example, if you find that the free instance is not meeting your needs, you can switch your notebook to a paid machine. You can also define multiple notebooks for different applications and configure auto-shutdown (how many hours your instance will run before shutting itself down) if you opt for a paid subscription. For details, see https://console.paperspace.com/teim6pi2i/upgrade.
- Select the START NOTEBOOK button to launch t...
Indice dei contenuti
- Deep Learning with fastai Cookbook
- Contributors
- Preface
- Chapter 1: Getting Started with fastai
- Chapter 2: Exploring and Cleaning Up Data with fastai
- Chapter 3: Training Models with Tabular Data
- Chapter 4: Training Models with Text Data
- Chapter 5: Training Recommender Systems
- Chapter 6: Training Models with Visual Data
- Chapter 7: Deployment and Model Maintenance
- Chapter 8: Extended fastai and Deployment Features
- Other Books You May Enjoy