AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence

Copyright
Table of Contents
Foreword
Preface
	Who Should Read This Book
	Why I Wrote This Book
	Navigating This Book
	Technology You Need to Understand
	Online Resources
	Conventions Used in This Book
	Using Code Examples
	O’Reilly Online Learning
	How to Contact Us
	Acknowledgments
Part I. Building Models
	Chapter 1. Introduction to TensorFlow
		What Is Machine Learning?
		Limitations of Traditional Programming
		From Programming to Learning
		What Is TensorFlow?
		Using TensorFlow
			Installing TensorFlow in Python
			Using TensorFlow in PyCharm
			Using TensorFlow in Google Colab
		Getting Started with Machine Learning
			Seeing What the Network Learned
		Summary
	Chapter 2. Introduction to Computer Vision
		Recognizing Clothing Items
			The Data: Fashion MNIST
		Neurons for Vision
		Designing the Neural Network
			The Complete Code
		Training the Neural Network
		Exploring the Model Output
		Training for Longer—Discovering Overfitting
		Stopping Training
		Summary
	Chapter 3. Going Beyond the Basics: Detecting Features in Images
		Convolutions
		Pooling
		Implementing Convolutional Neural Networks
		Exploring the Convolutional Network
		Building a CNN to Distinguish Between Horses and Humans
			The Horses or Humans Dataset
			The Keras ImageDataGenerator
			CNN Architecture for Horses or Humans
			Adding Validation to the Horses or Humans Dataset
			Testing Horse or Human Images
		Image Augmentation
		Transfer Learning
		Multiclass Classification
		Dropout Regularization
		Summary
	Chapter 4. Using Public Datasets with TensorFlow Datasets
		Getting Started with TFDS
		Using TFDS with Keras Models
			Loading Specific Versions
		Using Mapping Functions for Augmentation
			Using TensorFlow Addons
		Using Custom Splits
		Understanding TFRecord
		The ETL Process for Managing Data in TensorFlow
			Optimizing the Load Phase
			Parallelizing ETL to Improve Training Performance
		Summary
	Chapter 5. Introduction to Natural Language Processing
		Encoding Language into Numbers
			Getting Started with Tokenization
			Turning Sentences into Sequences
		Removing Stopwords and Cleaning Text
		Working with Real Data Sources
			Getting Text from TensorFlow Datasets
			Getting Text from CSV Files
			Getting Text from JSON Files
		Summary
	Chapter 6. Making Sentiment Programmable Using Embeddings
		Establishing Meaning from Words
			A Simple Example: Positives and Negatives
			Going a Little Deeper: Vectors
		Embeddings in TensorFlow
			Building a Sarcasm Detector Using Embeddings
			Reducing Overfitting in Language Models
			Using the Model to Classify a Sentence
		Visualizing the Embeddings
		Using Pretrained Embeddings from TensorFlow Hub
		Summary
	Chapter 7. Recurrent Neural Networks for Natural Language Processing
		The Basis of Recurrence
		Extending Recurrence for Language
		Creating a Text Classifier with RNNs
			Stacking LSTMs
		Using Pretrained Embeddings with RNNs
		Summary
	Chapter 8. Using TensorFlow to Create Text
		Turning Sequences into Input Sequences
		Creating the Model
		Generating Text
			Predicting the Next Word
			Compounding Predictions to Generate Text
		Extending the Dataset
		Changing the Model Architecture
		Improving the Data
		Character-Based Encoding
		Summary
	Chapter 9. Understanding Sequence and Time Series Data
		Common Attributes of Time Series
			Trend
			Seasonality
			Autocorrelation
			Noise
		Techniques for Predicting Time Series
			Naive Prediction to Create a Baseline
			Measuring Prediction Accuracy
			Less Naive: Using Moving Average for Prediction
			Improving the Moving Average Analysis
		Summary
	Chapter 10. Creating ML Models to Predict Sequences
		Creating a Windowed Dataset
			Creating a Windowed Version of the Time Series Dataset
		Creating and Training a DNN to Fit the Sequence Data
		Evaluating the Results of the DNN
		Exploring the Overall Prediction
		Tuning the Learning Rate
		Exploring Hyperparameter Tuning with Keras Tuner
		Summary
	Chapter 11. Using Convolutional and Recurrent Methods for Sequence Models
		Convolutions for Sequence Data
			Coding Convolutions
			Experimenting with the Conv1D Hyperparameters
		Using NASA Weather Data
			Reading GISS Data in Python
		Using RNNs for Sequence Modeling
			Exploring a Larger Dataset
		Using Other Recurrent Methods
		Using Dropout
		Using Bidirectional RNNs
		Summary
Part II. Using Models
	Chapter 12. An Introduction to TensorFlow Lite
		What Is TensorFlow Lite?
		Walkthrough: Creating and Converting a Model to TensorFlow Lite
			Step 1. Save the Model
			Step 2. Convert and Save the Model
			Step 3. Load the TFLite Model and Allocate Tensors
			Step 4. Perform the Prediction
		Walkthrough: Transfer Learning an Image Classifier and Converting to TensorFlow Lite
			Step 1. Build and Save the Model
			Step 2. Convert the Model to TensorFlow Lite
			Step 3. Optimize the Model
		Summary
	Chapter 13. Using TensorFlow Lite in Android Apps
		What Is Android Studio?
		Creating Your First TensorFlow Lite Android App
			Step 1. Create a New Android Project
			Step 2. Edit Your Layout File
			Step 3. Add the TensorFlow Lite Dependencies
			Step 4. Add Your TensorFlow Lite Model
			Step 5. Write the Activity Code to Use TensorFlow Lite for Inference
		Moving Beyond “Hello World”—Processing Images
		TensorFlow Lite Sample Apps
		Summary
	Chapter 14. Using TensorFlow Lite in iOS Apps
		Creating Your First TensorFlow Lite App with Xcode
			Step 1. Create a Basic iOS App
			Step 2. Add TensorFlow Lite to Your Project
			Step 3. Create the User Interface
			Step 4. Add and Initialize the Model Inference Class
			Step 5. Perform the Inference
			Step 6. Add the Model to Your App
			Step 7. Add the UI Logic
		Moving Beyond “Hello World”—Processing Images
		TensorFlow Lite Sample Apps
		Summary
	Chapter 15. An Introduction to TensorFlow.js
		What Is TensorFlow.js?
		Installing and Using the Brackets IDE
		Building Your First TensorFlow.js Model
		Creating an Iris Classifier
		Summary
	Chapter 16. Coding Techniques for Computer Vision in TensorFlow.js
		JavaScript Considerations for TensorFlow Developers
		Building a CNN in JavaScript
		Using Callbacks for Visualization
		Training with the MNIST Dataset
		Running Inference on Images in TensorFlow.js
		Summary
	Chapter 17. Reusing and Converting Python Models to JavaScript
		Converting Python-Based Models to JavaScript
			Using the Converted Models
		Using Preconverted JavaScript Models
			Using the Toxicity Text Classifier
			Using MobileNet for Image Classification in the Browser
			Using PoseNet
		Summary
	Chapter 18. Transfer Learning in JavaScript
		Transfer Learning from MobileNet
			Step 1. Download MobileNet and Identify the Layers to Use
			Step 2. Create Your Own Model Architecture with the Outputs from MobileNet as Its Input
			Step 3. Gather and Format the Data
			Step 4. Train the Model
			Step 5. Run Inference with the Model
		Transfer Learning from TensorFlow Hub
		Using Models from TensorFlow.org
		Summary
	Chapter 19. Deployment with TensorFlow Serving
		What Is TensorFlow Serving?
		Installing TensorFlow Serving
			Installing Using Docker
			Installing Directly on Linux
		Building and Serving a Model
			Exploring Server Configuration
		Summary
	Chapter 20. AI Ethics, Fairness, and Privacy
		Fairness in Programming
		Fairness in Machine Learning
		Tools for Fairness
			The What-If Tool
			Facets
		Federated Learning
			Step 1. Identify Available Devices for Training
			Step 2. Identify Suitable Available Devices for Training
			Step 3. Deploy a Trainable Model to Your Training Set
			Step 4. Return the Results of the Training to the Server
			Step 5. Deploy the New Master Model to the Clients
			Secure Aggregation with Federated Learning
			Federated Learning with TensorFlow Federated
		Google’s AI Principles
		Summary
Index
About the Author
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