Applied Machine Learning and AI for Engineers

Spis treści ebooka

• Foreword
• Preface
  • Who Should Read This Book
  • Why I Wrote This Book
  • Running the Books Code Samples
  • Navigating This Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. Machine Learning with Scikit-Learn
• 1. Machine Learning
  • What Is Machine Learning?
    • Machine Learning Versus Artificial Intelligence
    • Supervised Versus Unsupervised Learning
  • Unsupervised Learning with k-Means Clustering
    • Applying k-Means Clustering to Customer Data
    • Segmenting Customers Using More Than Two Dimensions
  • Supervised Learning
    • k-Nearest Neighbors
    • Using k-Nearest Neighbors to Classify Flowers
  • Summary
• 2. Regression Models
  • Linear Regression
  • Decision Trees
  • Random Forests
  • Gradient-Boosting Machines
  • Support Vector Machines
  • Accuracy Measures for Regression Models
  • Using Regression to Predict Taxi Fares
  • Summary
• 3. Classification Models
  • Logistic Regression
  • Accuracy Measures for Classification Models
  • Categorical Data
  • Binary Classification
    • Classifying Passengers Who Sailed on the Titanic
    • Detecting Credit Card Fraud
  • Multiclass Classification
  • Building a Digit Recognition Model
  • Summary
• 4. Text Classification
  • Preparing Text for Classification
  • Sentiment Analysis
  • Naive Bayes
  • Spam Filtering
  • Recommender Systems
    • Cosine Similarity
    • Building a Movie Recommendation System
  • Summary
• 5. Support Vector Machines
  • How Support Vector Machines Work
    • Kernels
    • Kernel Tricks
  • Hyperparameter Tuning
  • Data Normalization
  • Pipelining
  • Using SVMs for Facial Recognition
  • Summary
• 6. Principal Component Analysis
  • Understanding Principal Component Analysis
  • Filtering Noise
  • Anonymizing Data
  • Visualizing High-Dimensional Data
  • Anomaly Detection
    • Using PCA to Detect Credit Card Fraud
    • Using PCA to Predict Bearing Failure
    • Multivariate Anomaly Detection
  • Summary
• 7. Operationalizing Machine Learning Models
  • Consuming a Python Model from a Python Client
  • Versioning Pickle Files
  • Consuming a Python Model from a C# Client
  • Containerizing a Machine Learning Model
  • Using ONNX to Bridge the Language Gap
  • Building ML Models in C# with ML.NET
    • Sentiment Analysis with ML.NET
    • Saving and Loading ML.NET Models
  • Adding Machine Learning Capabilities to Excel
  • Summary
• II. Deep Learning with Keras and TensorFlow
• 8. Deep Learning
  • Understanding Neural Networks
  • Training Neural Networks
  • Summary
• 9. Neural Networks
  • Building Neural Networks with Keras and TensorFlow
    • Sizing a Neural Network
    • Using a Neural Network to Predict Taxi Fares
  • Binary Classification with Neural Networks
    • Making Predictions
    • Training a Neural Network to Detect Credit Card Fraud
  • Multiclass Classification with Neural Networks
  • Training a Neural Network to Recognize Faces
  • Dropout
  • Saving and Loading Models
  • Keras Callbacks
  • Summary
• 10. Image Classification with Convolutional Neural Networks
  • Understanding CNNs
    • Using Keras and TensorFlow to Build CNNs
    • Training a CNN to Recognize Arctic Wildlife
  • Pretrained CNNs
  • Using ResNet50V2 to Classify Images
  • Transfer Learning
  • Using Transfer Learning to Identify Arctic Wildlife
  • Data Augmentation
    • Image Augmentation with ImageDataGenerator
    • Image Augmentation with Augmentation Layers
    • Applying Image Augmentation to Arctic Wildlife
  • Global Pooling
  • Audio Classification with CNNs
  • Summary
• 11. Face Detection and Recognition
  • Face Detection
    • Face Detection with Viola-Jones
    • Using the OpenCV Implementation of Viola-Jones
    • Face Detection with Convolutional Neural Networks
    • Extracting Faces from Photos
  • Facial Recognition
    • Applying Transfer Learning to Facial Recognition
    • Boosting Transfer Learning with Task-Specific Weights
    • ArcFace
  • Putting It All Together: Detecting and Recognizing Faces in Photos
  • Handling Unknown Faces: Closed-Set Versus Open-Set Classification
  • Summary
• 12. Object Detection
  • R-CNNs
  • Mask R-CNN
  • YOLO
  • YOLOv3 and Keras
  • Custom Object Detection
    • Training a Custom Object Detection Model with the Custom Vision Service
    • Using the Exported Model
  • Summary
• 13. Natural Language Processing
  • Text Preparation
  • Word Embeddings
  • Text Classification
    • Automating Text Vectorization
    • Using TextVectorization in a Sentiment Analysis Model
    • Factoring Word Order into Predictions
    • Recurrent Neural Networks (RNNs)
    • Using Pretrained Models to Classify Text
  • Neural Machine Translation
    • LSTM Encoder-Decoders
    • Transformer Encoder-Decoders
    • Building a Transformer-Based NMT Model
    • Using Pretrained Models to Translate Text
  • Bidirectional Encoder Representations from Transformers (BERT)
    • Building a BERT-Based Question Answering System
    • Fine-Tuning BERT to Perform Sentiment Analysis
  • Summary
• 14. Azure Cognitive Services
  • Introducing Azure Cognitive Services
    • Keys and Endpoints
    • Calling Azure Cognitive Services APIs
    • Azure Cognitive Services Containers
  • The Computer Vision Service
  • The Language Service
  • The Translator Service
  • The Speech Service
  • Putting It All Together: Contoso Travel
  • Summary
• Index

pokaż cały spis treści

ukryj recenzje