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 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 fl...
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| Format: | eBook |
| Language: | English |
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Birmingham
Packt Publishing
2021
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| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- 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..
- 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
- 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