The Regularization Cookbook Explore Practical Recipes to Improve the Functionality of Your ML Models
Methodologies and recipes to regularize any machine learning and deep learning model using cutting-edge technologies such as stable diffusion, Dall-E and GPT-3 Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn to diagnose the need for regularization in any machine lea...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing Ltd
[2023]
|
| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009757931406719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credits
- Foreword
- Contributors
- Table of Contents
- Preface
- Chapter 1: An Overview of Regularization
- Technical requirements
- Introducing regularization
- Examples of models that did not pass the deployment test
- Intuition about regularization
- Key concepts of regularization
- Bias and variance
- Underfitting and overfitting
- Regularization - from overfitting to underfitting
- Unavoidable bias
- Diagnosing bias and variance
- Regularization - a multi-dimensional problem
- Summary
- Chapter 2: Machine Learning Refresher
- Technical requirements
- Loading data
- Getting ready
- How to do it…
- There's more…
- See also
- Splitting data
- Getting ready
- How to do it…
- See also
- Preparing quantitative data
- Getting ready
- How to do it…
- There's more…
- See also
- Preparing qualitative data
- Getting ready
- How to do it…
- There's more…
- See also
- Training a model
- Getting ready
- How to do it…
- See also
- Evaluating a model
- Getting ready
- How to do it…
- See also
- Performing hyperparameter optimization
- Getting ready
- How to do it…
- Chapter 3: Regularization with Linear Models
- Technical requirements
- Training a linear regression model with scikit-learn
- Getting ready
- How to do it…
- There's more…
- See also
- Regularizing with ridge regression
- Getting ready
- How to do it…
- There's more…
- See also
- Regularizing with lasso regression
- Getting ready
- How to do it…
- There's more…
- See also
- Regularizing with elastic net regression
- Getting ready
- How to do it…
- See also
- Training a logistic regression model
- Getting ready
- How to do it…
- Regularizing a logistic regression model
- Getting ready
- How to do it…
- There's more…
- Choosing the right regularization
- Getting ready.
- How to do it…
- See also
- Chapter 4: Regularization with Tree-Based Models
- Technical requirements
- Building a classification tree
- Disorder measurement
- Loss function
- Getting ready
- How to do it…
- There's more…
- See also
- Building regression trees
- Getting ready
- How to do it…
- See also
- Regularizing a decision tree
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Training the Random Forest algorithm
- Getting ready
- How to do it…
- See also
- Regularization of Random Forest
- Getting started
- How to do it…
- Training a boosting model with XGBoost
- Getting ready
- How to do it…
- See also
- Regularization with XGBoost
- Getting ready
- How to do it…
- There's more…
- Chapter 5: Regularization with Data
- Technical requirements
- Hashing high cardinality features
- Getting started
- How to do it...
- See also
- Aggregating features
- Getting ready
- How to do it...
- There's more...
- Undersampling an imbalanced dataset
- Getting ready
- How to do it...
- There's more...
- See also
- Oversampling an imbalanced dataset
- Getting ready
- How to do it...
- There's more...
- See also
- Resampling imbalanced data with SMOTE
- Getting ready
- How to do it...
- There's more...
- See also
- Chapter 6: Deep Learning Reminders
- Technical requirements
- Training a perceptron
- Getting started
- How to do it…
- There's more…
- See also
- Training a neural network for regression
- Getting started
- How to do it…
- There's more…
- See also
- Training a neural network for binary classification
- Getting ready
- How to do it…
- There's more…
- See also
- Training a multiclass classification neural network
- Getting ready
- How to do it…
- There's more…
- See also
- Chapter 7: Deep Learning Regularization
- Technical requirements.
- Regularizing a neural network with L2 regularization
- Getting ready
- How to do it...
- There's more...
- See also
- Regularizing a neural network with early stopping
- Getting ready
- How to do it...
- There's more...
- Regularization with network architecture
- Getting ready
- How to do it...
- There's more...
- Regularizing with dropout
- Getting ready
- How to do it...
- There's more...
- See also
- Chapter 8: Regularization with Recurrent Neural Networks
- Technical requirements
- Training an RNN
- Getting started
- How to do it…
- There's more…
- See also
- Training a GRU
- Getting started
- How to do it…
- There's more…
- See also
- Regularizing with dropout
- Getting ready
- How to do it…
- There's more…
- Regularizing with the maximum sequence length
- Getting ready
- How to do it…
- There's more…
- Chapter 9: Advanced Regularization in Natural Language Processing
- Technical requirements
- Regularization using a word2vec embedding
- Getting ready
- How to do it…
- There's more…
- See also
- Data augmentation using word2vec
- Getting ready
- How to do it…
- There's more…
- See also
- Zero-shot inference with pre-trained models
- Getting ready
- How to do it…
- There's more…
- See also
- Regularization with BERT embeddings
- Getting ready
- How to do it…
- There's more…
- See also
- Data augmentation using GPT-3
- Getting ready
- How to do it…
- There's more…
- See also
- Chapter 10: Regularization in Computer Vision
- Technical requirements
- Training a CNN
- Getting started
- How to do it…
- There's more…
- See also
- Regularizing a CNN with vanilla NN methods
- Getting started
- How to do it…
- There's more…
- See also
- Regularizing a CNN with transfer learning for object detection
- Object detection
- Mean average precision
- COCO dataset.
- Getting started
- How to do it…
- There's more…
- See also
- Semantic segmentation using transfer learning
- Getting started
- How to do it…
- There's more…
- See also
- Chapter 11: Regularization in Computer Vision - Synthetic Image Generation
- Technical requirements
- Applying image augmentation with Albumentations
- Spatial-level augmentation
- Pixel-level augmentation
- Albumentations
- Getting started
- How to do it…
- There's more…
- See also
- Creating synthetic images for object detection
- Getting started
- How to do it…
- There's more…
- See also
- Implementing real-time style transfer
- Stable Diffusion
- Perceptual loss
- Getting started
- How to do it…
- There's more…
- See also
- Index
- Other Books You May Enjoy.
