دانلود کتاب The Regularization Cookbook: Explore practical recipes to improve the functionality of your ML models [Team-IRA]

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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
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