UID:
almahu_9949708240702882
Format:
1 online resource (510 pages)
Edition:
1st ed.
ISBN:
1394220642
,
9781394220649
,
1394220634
,
9781394220632
Note:
Cover -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Foreword -- Acknowledgments -- General Introduction -- Chapter 1 Concepts, Libraries, and Essential Tools in Machine Learning and Deep Learning -- 1.1 Learning Styles for Machine Learning -- 1.1.1 Supervised Learning -- 1.1.1.1 Overfitting and Underfitting -- 1.1.1.2 K-Folds Cross-Validation -- 1.1.1.3 Train/Test Split -- 1.1.1.4 Confusion Matrix -- 1.1.1.5 Loss Functions -- 1.1.2 Unsupervised Learning -- 1.1.3 Semi-Supervised Learning -- 1.1.4 Reinforcement Learning -- 1.2 Essential Python Tools for Machine Learning -- 1.2.1 Data Manipulation with Python -- 1.2.2 Python Machine Learning Libraries -- 1.2.2.1 Scikit-learn -- 1.2.2.2 TensorFlow -- 1.2.2.3 Keras -- 1.2.2.4 PyTorch -- 1.2.3 Jupyter Notebook and JupyterLab -- 1.3 HephAIstos for Running Machine Learning on CPUs, GPUs, and QPUs -- 1.3.1 Installation -- 1.3.2 HephAIstos Function -- 1.4 Where to Find the Datasets and Code Examples -- Further Reading -- Chapter 2 Feature Engineering Techniques in Machine Learning -- 2.1 Feature Rescaling: Structured Continuous Numeric Data -- 2.1.1 Data Transformation -- 2.1.1.1 StandardScaler -- 2.1.1.2 MinMaxScaler -- 2.1.1.3 MaxAbsScaler -- 2.1.1.4 RobustScaler -- 2.1.1.5 Normalizer: Unit Vector Normalization -- 2.1.1.6 Other Options -- 2.1.1.7 Transformation to Improve Normal Distribution -- 2.1.1.8 Quantile Transformation -- 2.1.2 Example: Rescaling Applied to an SVM Model -- 2.2 Strategies to Work with Categorical (Discrete) Data -- 2.2.1 Ordinal Encoding -- 2.2.2 One-Hot Encoding -- 2.2.3 Label Encoding -- 2.2.4 Helmert Encoding -- 2.2.5 Binary Encoding -- 2.2.6 Frequency Encoding -- 2.2.7 Mean Encoding -- 2.2.8 Sum Encoding -- 2.2.9 Weight of Evidence Encoding -- 2.2.10 Probability Ratio Encoding -- 2.2.11 Hashing Encoding -- 2.2.12 Backward Difference Encoding.
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2.2.13 Leave-One-Out Encoding -- 2.2.14 James-Stein Encoding -- 2.2.15 M-Estimator Encoding -- 2.2.16 Using HephAIstos to Encode Categorical Data -- 2.3 Time-Related Features Engineering -- 2.3.1 Date-Related Features -- 2.3.2 Lag Variables -- 2.3.3 Rolling Window Feature -- 2.3.4 Expending Window Feature -- 2.3.5 Understanding Time Series Data in Context -- 2.4 Handling Missing Values in Machine Learning -- 2.4.1 Row or Column Removal -- 2.4.2 Statistical Imputation: Mean, Median, and Mode -- 2.4.3 Linear Interpolation -- 2.4.4 Multivariate Imputation by Chained Equation Imputation -- 2.4.5 KNN Imputation -- 2.5 Feature Extraction and Selection -- 2.5.1 Feature Extraction -- 2.5.1.1 Principal Component Analysis -- 2.5.1.2 Independent Component Analysis -- 2.5.1.3 Linear Discriminant Analysis -- 2.5.1.4 Locally Linear Embedding -- 2.5.1.5 The t-Distributed Stochastic Neighbor Embedding Technique -- 2.5.1.6 More Manifold Learning Techniques -- 2.5.1.7 Feature Extraction with HephAIstos -- 2.5.2 Feature Selection -- 2.5.2.1 Filter Methods -- 2.5.2.2 Wrapper Methods -- 2.5.2.3 Embedded Methods -- 2.5.2.4 Feature Importance Using Graphics Processing Units (GPUs) -- 2.5.2.5 Feature Selection Using HephAIstos -- Further Reading -- Chapter 3 Machine Learning Algorithms -- 3.1 Linear Regression -- 3.1.1 The Math -- 3.1.2 Gradient Descent to Optimize the Cost Function -- 3.1.3 Implementation of Linear Regression -- 3.1.3.1 Univariate Linear Regression -- 3.1.3.2 Multiple Linear Regression: Predicting Water Temperature -- 3.2 Logistic Regression -- 3.2.1 Binary Logistic Regression -- 3.2.1.1 Cost Function -- 3.2.1.2 Gradient Descent -- 3.2.2 Multinomial Logistic Regression -- 3.2.3 Multinomial Logistic Regression Applied to Fashion MNIST -- 3.2.3.1 Logistic Regression with scikit-learn -- 3.2.3.2 Logistic Regression with Keras on TensorFlow.
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3.2.4 Binary Logistic Regression with Keras on TensorFlow -- 3.3 Support Vector Machine -- 3.3.1 Linearly Separable Data -- 3.3.2 Not Fully Linearly Separable Data -- 3.3.3 Nonlinear SVMs -- 3.3.4 SVMs for Regression -- 3.3.5 Application of SVMs -- 3.3.5.1 SVM Using scikit-learn for Classification -- 3.3.5.2 SVM Using scikit-learn for Regression -- 3.4 Artificial Neural Networks -- 3.4.1 Multilayer Perceptron -- 3.4.2 Estimation of the Parameters -- 3.4.2.1 Loss Functions -- 3.4.2.2 Backpropagation: Binary Classification -- 3.4.2.3 Backpropagation: Multi-class Classification -- 3.4.3 Convolutional Neural Networks -- 3.4.4 Recurrent Neural Network -- 3.4.5 Application of MLP Neural Networks -- 3.4.6 Application of RNNs: LST Memory -- 3.4.7 Building a CNN -- 3.5 Many More Algorithms to Explore -- 3.6 Unsupervised Machine Learning Algorithms -- 3.6.1 Clustering -- 3.6.1.1 K-means -- 3.6.1.2 Mini-batch K-means -- 3.6.1.3 Mean Shift -- 3.6.1.4 Affinity Propagation -- 3.6.1.5 Density-based Spatial Clustering of Applications with Noise -- 3.7 Machine Learning Algorithms with HephAIstos -- References -- Further Reading -- Chapter 4 Natural Language Processing -- 4.1 Classifying Messages as Spam or Ham -- 4.2 Sentiment Analysis -- 4.3 Bidirectional Encoder Representations from Transformers -- 4.4 BERT's Functionality -- 4.5 Installing and Training BERT for Binary Text Classification Using TensorFlow -- 4.6 Utilizing BERT for Text Summarization -- 4.7 Utilizing BERT for Question Answering -- Further Reading -- Chapter 5 Machine Learning Algorithms in Quantum Computing -- 5.1 Quantum Machine Learning -- 5.2 Quantum Kernel Machine Learning -- 5.3 Quantum Kernel Training -- 5.4 Pegasos QSVC: Binary Classification -- 5.5 Quantum Neural Networks -- 5.5.1 Binary Classification with EstimatorQNN -- 5.5.2 Classification with a SamplerQNN.
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5.5.3 Classification with Variational Quantum Classifier -- 5.5.4 Regression -- 5.6 Quantum Generative Adversarial Network -- 5.7 Quantum Algorithms with HephAIstos -- References -- Further Reading -- Chapter 6 Machine Learning in Production -- 6.1 Why Use Docker Containers for Machine Learning? -- 6.1.1 First Things First: The Microservices -- 6.1.2 Containerization -- 6.1.3 Docker and Machine Learning: Resolving the "It Works in My Machine" Problem -- 6.1.4 Quick Install and First Use of Docker -- 6.1.4.1 Install Docker -- 6.1.4.2 Using Docker from the Command Line -- 6.1.5 Dockerfile -- 6.1.6 Build and Run a Docker Container for Your Machine Learning Model -- 6.2 Machine Learning Prediction in Real Time Using Docker and Python REST APIs with Flask -- 6.2.1 Flask-RESTful APIs -- 6.2.2 Machine Learning Models -- 6.2.3 Docker Image for the Online Inference -- 6.2.4 Running Docker Online Inference -- 6.3 From DevOps to MLOPS: Integrate Machine Learning Models Using Jenkins and Docker -- 6.3.1 Jenkins Installation -- 6.3.2 Scenario Implementation -- 6.4 Machine Learning with Docker and Kubernetes: Install a Cluster from Scratch -- 6.4.1 Kubernetes Vocabulary -- 6.4.2 Kubernetes Quick Install -- 6.4.3 Install a Kubernetes Cluster -- 6.4.4 Kubernetes: Initialization and Internal Network -- 6.5 Machine Learning with Docker and Kubernetes: Training Models -- 6.5.1 Kubernetes Jobs: Model Training and Batch Inference -- 6.5.2 Create and Prepare the Virtual Machines -- 6.5.3 Kubeadm Installation -- 6.5.4 Create a Kubernetes Cluster -- 6.5.5 Containerize our Python Application that Trains Models -- 6.5.6 Create Configuration Files for Kubernetes -- 6.5.7 Commands to Delete the Cluster -- 6.6 Machine Learning with Docker and Kubernetes: Batch Inference -- 6.6.1 Create Configuration Files for Kubernetes.
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6.7 Machine Learning Prediction in Real Time Using Docker, Python Rest APIs with Flask, and Kubernetes: Online Inference -- 6.7.1 Flask-RESTful APIs -- 6.7.2 Machine Learning Models -- 6.7.3 Docker Image for Online Inference -- 6.7.4 Running Docker Online Inference -- 6.7.5 Create and Prepare the Virtual Machines -- 6.7.6 Kubeadm Installation -- 6.7.7 Create a Kubernetes Cluster -- 6.7.8 Deploying the Containerized Machine Learning Model to Kubernetes -- 6.8 A Machine Learning Application that Deploys to the IBM Cloud Kubernetes Service: Python, Docker, Kubernetes -- 6.8.1 Create Kubernetes Service on IBM Cloud -- 6.8.2 Containerization of a Machine Learning Application -- 6.8.3 Push the Image to the IBM Cloud Registry -- 6.8.4 Deploy the Application to Kubernetes -- 6.9 Red Hat OpenShift to Develop and Deploy Enterprise ML/DL Applications -- 6.9.1 What is OpenShift? -- 6.9.2 What Is the Difference Between OpenShift and Kubernetes? -- 6.9.3 Why Red Hat OpenShift for ML/DL? To Build a Production-Ready ML/DL Environment -- 6.10 Deploying a Machine Learning Model as an API on the Red Hat OpenShift Container Platform: From Source Code in a GitHub Repository with Flask, Scikit-Learn, and Docker -- 6.10.1 Create an OpenShift Cluster Instance -- 6.10.1.1 Deploying an Application from Source Code in a GitHub Repository -- Further Reading -- Conclusion: The Future of Computing for Data Science? -- Index -- EULA.
Additional Edition:
Print version: Vasques, Xavier Machine Learning Theory and Applications Newark : John Wiley & Sons, Incorporated,c2024 ISBN 9781394220618
Language:
English
DOI:
10.1002/9781394220649
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9781394220649
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9781394220649
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9781394220649
