Data augmentation with Python enhance accuracy in deep learning with practical data augmentation for image, text, audio and tabular data
Boost your AI and generative AI accuracy using real-world datasets with over 150 functional object-oriented methods and open source libraries Purchase of the print or Kindle book includes a free PDF eBook Key Features Explore beautiful, customized charts and infographics in full color Work with full...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing
[2023]
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| Edición: | 1st ed |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009742735506719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright
- Dedication
- Foreword
- Contributors
- Table of Contents
- Preface
- Part 1: Data Augmentation
- Chapter 1: Data Augmentation Made Easy
- Data augmentation role
- Data input types
- Image definition
- Text definition
- Audio definition
- Tabular data definition
- Python Notebook
- Google Colab
- Additional Python Notebook options
- Installing Python Notebook
- Programming styles
- Source control
- The PacktDataAug class
- Naming convention
- Extend base class
- Referencing a library
- Exporting Python code
- Pluto
- Summary
- Chapter 2: Biases in Data Augmentation
- Computational biases
- Human biases
- Systemic biases
- Python Notebook
- Python Notebook
- GitHub
- Pluto
- Verifying Pluto
- Kaggle ID
- Image biases
- State Farm distracted drivers detection
- Nike shoes
- Grapevine leaves
- Text biases
- Netflix
- Amazon reviews
- Summary
- Part 2: Image Augmentation
- Chapter 3: Image Augmentation for Classification
- Geometric transformations
- Flipping
- Cropping
- Resizing
- Padding
- Rotating
- Translation
- Noise injection
- Photometric transformations
- Basic and classic
- Advanced and exotic
- Random erasing
- Combining
- Reinforcing your learning through Python code
- Pluto and the Python Notebook
- Real-world image datasets
- Image augmentation library
- Geometric transformation filters
- Photographic transformations
- Random erasing
- Combining
- Summary
- Chapter 4: Image Augmentation for Segmentation
- Geometric and photometric transformations
- Real-world segmentation datasets
- Python Notebook and Pluto
- Real-world data
- Pandas
- Viewing data images
- Reinforcing your learning
- Horizontal flip
- Vertical flip
- Rotating
- Resizing and cropping
- Transpose
- Lighting
- FancyPCA
- Combining
- Summary.
- Part 3: Text Augmentation
- Chapter 5: Text Augmentation
- Character augmenting
- Word augmenting
- Sentence augmentation
- Text augmentation libraries
- Real-world text datasets
- The Python Notebook and Pluto
- Real-world NLP datasets
- Pandas
- Visualizing NLP data
- Reinforcing learning through Python Notebook
- Character augmentation
- Word augmenting
- Summary
- Chapter 6: Text Augmentation with Machine Learning
- Machine learning models
- Word augmenting
- Sentence augmenting
- Real-world NLP datasets
- Python Notebook and Pluto
- Verify
- Real-world NLP data
- Pandas
- Viewing the text
- Reinforcing your learning through the Python Notebook
- Word2Vec word augmenting
- BERT
- RoBERTa
- Back translation
- Sentence augmentation
- Summary
- Part 4: Audio Data Augmentation
- Chapter 7: Audio Data Augmentation
- Standard audio augmentation techniques
- Time stretching
- Time shifting
- Pitch shifting
- Polarity inversion
- Noise injection
- Filters
- Low-pass filter
- High-pass filter
- Band-pass filter
- Low-shelf filter
- High-shelf filter
- Band-stop filter
- Peak filter
- Audio augmentation libraries
- Real-world audio datasets
- Python Notebook and Pluto
- Real-world data and pandas
- Listening and viewing
- Reinforcing your learning
- Time shifting
- Time stretching
- Pitch scaling
- Noise injection
- Polarity inversion
- Low-pass filter
- Band-pass filter
- High-pass and other filters
- Summary
- Chapter 8: Audio Data Augmentation with Spectrogram
- Initializing and downloading
- Audio Spectrogram
- Various Spectrogram formats
- Mel-spectrogram and Chroma STFT plots
- Spectrogram augmentation
- Spectrogram images
- Summary
- Part 5: Tabular Data Augmentation
- Chapter 9: Tabular Data Augmentation
- Tabular augmentation libraries
- Augmentation categories.
- Real-world tabular datasets
- Exploring and visualizing tabular data
- Data structure
- First graph view
- Checksum
- Specialized plots
- Exploring the World Series data
- Transforming augmentation
- Robust scaler
- Standard scaler
- Capping
- Interaction augmentation
- Regression augmentation
- Operator augmentation
- Mapping augmentation
- Extraction augmentation
- Summary
- Index
- About Packt
- Other Books You May Enjoy.
