Jeff Prosise
Applied Machine Learning and AI for Engineers
Solve Business Problems That Can't Be Solved Algorithmically
Paperback Engels 2022 1e druk 9781492098058Levertijd ± 16 werkdagen
Gratis verzonden
Samenvatting
While many introductory guides to AI are calculus books in disguise, this one mostly eschews the math. Instead, author Jeff Prosise helps engineers and software developers build an intuitive understanding of AI to solve business problems. Need to create a system to detect the sounds of illegal logging in the rainforest, analyze text for sentiment, or predict early failures in rotating machinery? This practical book teaches you the skills necessary to put AI and machine learning to work at your company.
Applied Machine Learning and AI for Engineers provides examples and illustrations from the AI and ML course Prosise teaches at companies and research institutions worldwide. There's no fluff and no scary equations—just a fast start for engineers and software developers, complete with hands-on examples.
This book helps you:
- Learn what machine learning and deep learning are and what they can accomplish
- Understand how popular learning algorithms work and when to apply them
- Build machine learning models in Python with Scikit-Learn, and neural networks with Keras and TensorFlow
- Train and score regression models and binary and multiclass classification models
- Build facial recognition models and object detection models
- Build language models that respond to natural-language queries and translate text to other languages
- Use Cognitive Services to infuse AI into the apps that you write
Specificaties
ISBN13:9781492098058
Taal:Engels
Bindwijze:paperback
Aantal pagina's:300
Uitgever:O'Reilly
Druk:1
Verschijningsdatum:30-11-2022
Hoofdrubriek:IT-management / ICT
Thema:
Lezersrecensies
Wees de eerste die een lezersrecensie schrijft!
Inhoudsopgave
Foreword
Preface
Who Should Read This Book
Why I Wrote This Book
Running the Book's Code Samples
Navigating This Book
Conventions Used in This Book
Using Code Examples
O'Reilly Online Learning
How to Contact Us
Acknowledgments
Part 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
Preface
Who Should Read This Book
Why I Wrote This Book
Running the Book's Code Samples
Navigating This Book
Conventions Used in This Book
Using Code Examples
O'Reilly Online Learning
How to Contact Us
Acknowledgments
Part 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
Part 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
About the Author
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
Part 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
About the Author
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