AI and Machine Learning for Coders

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• 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
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. Building Models
• 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
• 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 LongerDiscovering Overfitting
  • Stopping Training
  • Summary
• 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
• 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
• 5. Introduction to Natural Language Processing
  • Encoding Language into Numbers
    • Getting Started with Tokenization
    • Turning Sentences into Sequences
      • Using out-of-vocabulary tokens
      • Understanding padding
  • Removing Stopwords and Cleaning Text
  • Working with Real Data Sources
    • Getting Text from TensorFlow Datasets
      • Using the IMDb subwords datasets
    • Getting Text from CSV Files
      • Creating training and test subsets
    • Getting Text from JSON Files
      • Reading JSON files
  • Summary
• 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
      • Adjusting the learning rate
      • Exploring vocabulary size
      • Exploring embedding dimensions
      • Exploring the model architecture
      • Using dropout
      • Using regularization
      • Other optimization considerations
    • Using the Model to Classify a Sentence
  • Visualizing the Embeddings
  • Using Pretrained Embeddings from TensorFlow Hub
  • Summary
• 7. Recurrent Neural Networks for Natural Language Processing
  • The Basis of Recurrence
  • Extending Recurrence for Language
  • Creating a Text Classifier with RNNs
    • Stacking LSTMs
      • Optimizing stacked LSTMs
      • Using dropout
  • Using Pretrained Embeddings with RNNs
  • Summary
• 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
• 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
• 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
• 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
• II. Using Models
• 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
• 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 WorldProcessing Images
  • TensorFlow Lite Sample Apps
  • Summary
• 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 WorldProcessing Images
  • TensorFlow Lite Sample Apps
  • Summary
• 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
• 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
• 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
• 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
• 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
• 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
  • Googles AI Principles
  • Summary
• Index

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