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Automated Machine Learning with AutoKeras
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Automated Machine Learning with AutoKeras
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Automated Machine Learning with AutoKeras
Automated Machine Learning with AutoKeras
Contributors
About the author
About the reviewers
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Section 1: AutoML Fundamentals
Chapter 1: Introduction to Automated Machine Learning
The anatomy of a standard ML workflow
Data ingestion
Data preprocessing
Model deployment
Model monitoring
What is AutoML?
Differences from the standard approach
Types of AutoML
Automated feature engineering
Automated model choosing and hyperparameter optimization
Automated neural network architecture selection
Summary
Further reading
Chapter 2: Getting Started with AutoKeras
Technical requirements
What is deep learning?
What is a neural network and how does it learn?
How do deep learning models learn?
Why AutoKeras?
How to run the AutoKeras experiments?
Installing AutoKeras
Installing AutoKeras in the cloud
Installing AutoKeras locally
Hello MNIST: Implementing our first AutoKeras experiment
Importing the needed packages
Getting the MNIST dataset
How are the digits distributed?
Creating an image classifier
Evaluating the model with the test set
Visualizing the model
Creating an image regressor
Evaluating the model with the test set
Visualizing the model
Summary
Chapter 3: Automating the Machine Learning Pipeline with AutoKeras
Understanding tensors
What is a tensor?
Types of tensors
Preparing the data to feed deep learning models
Data preprocessing operations for neural network models
Loading data into AutoKeras in multiple formats
Splitting your dataset for training and evaluation
Why you should split your dataset
How to split your dataset
Summary
Section 2: AutoKeras in Practice
Chapter 4: Image Classification and Regression Using AutoKeras
Technical requirements
Surpassing classical neural networks
Creating a CIFAR-10 image classifier
Creating and fine-tuning a powerful image classifier
Improving the model performance
Evaluating the model with the test set
Visualizing the model
Creating an image regressor to find out the age of people
Creating and fine-tuning a powerful image regressor
Improving the model performance
Evaluating the model with the test set
Visualizing the model
Summary
Chapter 6: Working with Structured Data Using AutoKeras
Technical requirements
Understanding structured data
Working with structured data
Creating a structured data classifier to predict Titanic survivors
Creating the classifier
Evaluating the model
Visualizing the model
Creating a structured data regressor to predict Boston house prices
Creating a structure data regressor
Evaluating the model
Visualizing the model
Summary
Chapter 7: Sentiment Analysis Using AutoKeras
Technical requirements
Creating a sentiment analyzer
Creating the sentiment predictor
Evaluating the model
Visualizing the model
Analyzing the sentiment in specific sentences
Summary
Chapter 8: Topic Classification Using AutoKeras
Technical requirements
Understanding topic classification
Creating a news topic classifier
Creating the classifier
Evaluating the model
Visualizing the model
Evaluating the model
Customizing the model search space
Summary
Section 3: Advanced AutoKeras
Chapter 9: Working with Multimodal and Multitasking Data
Technical requirements
Exploring models with multiple inputs or outputs
What is AutoModel?
What is multimodal?
What is multitask?
Creating a multitask/multimodal model
Creating the model
Visualizing the model
Customizing the search space
Summary
Chapter 10: Exporting and Visualizing the Models
Technical requirements
Exporting your models
How to save and load a model
Visualizing your models with TensorBoard
Using callbacks to log the model state
Setting up and loading TensorBoard
Sharing your ML experiment results with TensorBoard.dev
Visualizing and comparing your models with ClearML
Adding ClearML to code
Comparing experiments
Summary
A final few words
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