Deep learning with fastai cookbook leverage the easy-to-use fastai framework to unlock the power of deep learning
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 flexi...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England ; Mumbai :
Packt
[2021]
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009644273506719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credits
- Contributors
- Table of Contents
- Preface
- Chapter 1: Getting Started with fastai
- Technical requirements
- Setting up a fastai environment in Paperspace Gradient
- Getting ready
- How to do it...
- How it works...
- There's more...
- Setting up a fastai environment in Google Colab
- Getting ready
- How to do it...
- How it works...
- There's more...
- Setting up JupyterLab environment in Gradient
- Getting ready
- How to do it...
- How it works...
- There's more...
- Hello world" for fastai - creating a model for MNIST
- Getting ready...
- How to do it...
- How it works...
- There's more...
- Understanding the world in four applications: tables, text, recommender systems, and images
- Getting ready
- How to do it...
- How it works...
- Working with PyTorch tensors
- Getting ready
- How to do it...
- How it works...
- There's more...
- Contrasting fastai with Keras
- Getting ready
- How to do it...
- How it works...
- Test your knowledge
- Chapter 2: Exploring and Cleaning Up Data with fastai
- Technical requirements
- Getting the complete set of oven-ready fastai datasets
- Getting ready
- How to do it...
- How it works...
- There's more...
- Examining tabular datasets with fastai
- Getting ready
- How to do it...
- How it works...
- There's more...
- Examining text datasets with fastai
- Getting ready
- How to do it...
- How it works...
- Examining image datasets with fastai
- Getting ready
- How to do it...
- How it works...
- There's more...
- Cleaning up raw datasets with fastai
- Getting ready
- How to do it...
- How it works...
- Chapter 3: Training Models with Tabular Data
- Technical requirements
- Training a model in fastai with a curated tabular dataset
- Getting ready
- How to do it...
- How it works...
- Training a model in fastai with a non-curated tabular dataset
- Getting ready.
- How to do it...
- How it works...
- Training a model with a standalone dataset
- Getting ready
- How to do it...
- How it works...
- Assessing whether a tabular dataset is a good candidate for fastai
- Getting ready
- How to do it...
- How it works...
- Saving a trained tabular model
- Getting ready
- How to do it...
- How it works...
- Test your knowledge
- Getting ready
- Chapter 4: Training Models with Text Data
- Technical requirements
- Training a deep learning language model with a curated IMDb text dataset
- Getting ready
- How to do it...
- How it works...
- There's more...
- Training a deep learning classification model with a curated text dataset
- Getting ready
- How to do it...
- How it works...
- There's more...
- Training a deep learning language model with a standalone text dataset
- Getting ready
- How to do it...
- How it works...
- Training a deep learning text classifier with a standalone text dataset
- Getting ready
- How to do it...
- How it works...
- Test your knowledge
- Getting ready
- How to do it...
- Chapter 5: Training Recommender Systems
- Technical requirements
- Training a recommender system on a small curated dataset
- Getting ready
- How to do it...
- How it works...
- Training a recommender system on a large curated dataset
- Getting ready
- How to do it...
- How it works...
- Training a recommender system on a standalone dataset
- Getting ready
- How to do it...
- How it works...
- Test your knowledge
- Getting ready
- How to do it...
- Chapter 6: Training Models with Visual Data
- Technical requirements
- Training a classification model with a simple curated vision dataset
- Getting ready
- How to do it...
- How it works...
- Exploring a curated image location dataset
- Getting ready
- How to do it...
- How it works...
- Training a classification model with a standalone vision dataset
- Getting ready.
- How to do it...
- How it works...
- Training a multi-image classification model with a curated vision dataset
- Getting ready
- How to do it...
- How it works...
- Test your knowledge
- Getting ready
- How to do it...
- Chapter 7: Deployment and Model Maintenance
- Technical requirements
- Setting up fastai on your local system
- Getting ready
- How to do it...
- How it works...
- Deploying a fastai model trained on a tabular dataset
- Getting ready
- How to do it...
- How it works...
- There's more...
- Deploying a fastai model trained on an image dataset
- Getting ready
- How to do it...
- How it works...
- There's more...
- Maintaining your fastai model
- Getting ready
- How to do it...
- How it works...
- There's more...
- Test your knowledge
- Getting ready
- How to do it...
- Chapter 8: Extended fastai and Deployment Features
- Technical requirements
- Getting more details about models trained with tabular data
- Getting ready
- How to do it...
- How it works...
- Getting more details about image classification models
- Getting ready
- How to do it...
- How it works...
- Training a model with augmented data
- Getting ready
- How to do it...
- How it works...
- Using callbacks to get the most out of your training cycle
- Getting ready
- How to do it...
- How it works...
- Making your model deployments available to others
- Getting ready
- How to do it...
- How it works...
- Displaying thumbnails in your image classification model deployment
- Getting ready
- How to do it...
- How it works...
- Test your knowledge
- Explore the value of repeatable results
- Displaying multiple thumbnails in your image classification model deployment
- Conclusion and additional resources on fastai
- Other Books You May Enjoy
- Index.
