From technical specialist to supervisor [[electronic resource] /] / Donald Shandler

• Record Nr.: UNINA9910455652203321
• Autore: Shandler Donald <1940->
• Pubbl/distr/stampa: Menlo Park, Calif., : Crisp Publications, c1993
• ISBN: 1-4175-2188-0
• Descrizione fisica: 1 online resource (134 p.)
• Collana: Fifty-Minute series
• Disciplina: 658.3/02
• Soggetti: Supervision of employees; Career development; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: "Making the successful transition"--Cover.
• Nota di bibliografia: Includes bibliographical references (p. 113).

Introduction to machine learning in the cloud with Python : concepts and practices / / Pramod Gupta, Naresh K. Sehgal

• Record Nr.: UNINA9910483986303321
• Autore: Gupta Pramod
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2021]; ©2021
• ISBN: 3-030-71270-2
• Descrizione fisica: 1 online resource (293 pages)
• Disciplina: 006.31
• Soggetti: Machine learning
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- Foreword -- Preface -- About the Book -- Contents -- Acronyms -- Part I: Concepts -- Chapter 1: Machine Learning Concepts -- 1.1 Terminology -- 1.2 What Is Machine Learning? -- 1.2.1 Mitchell´s Notion of Machine Learning -- 1.3 What Does Learning Mean for a Computer? -- 1.4 Difference Between ML and Traditional Programming -- 1.5 How Do Machines Learn? -- 1.6 Steps to Apply ML -- 1.7 Paradigms of Learning -- 1.7.1 Supervised Machine Learning -- 1.7.2 Unsupervised Machine Learning -- 1.7.3 Reinforcement Machine Learning -- 1.7.3.1 Types of Problems in Machine Learning -- 1.8 Machine Learning in Practice -- 1.9 Why Use Machine Learning? -- 1.10 Why Machine Learning Now? -- 1.11 Classical Tasks for Machine Learning -- 1.12 Applications of Machine Learning -- 1.12.1 Applications in Our Daily Life -- 1.13 ML Computing Needs -- 1.14 Machine Learning in the Cloud -- 1.15 Tools Used in Machine Learning -- 1.16 Points to Ponder -- References -- Chapter 2: Machine Learning Algorithms -- 2.1 Why Choose Machine Learning? -- 2.2 Supervised Machine Learning Algorithms -- 2.2.1 Regression -- 2.2.2 Classification -- 2.2.3 Machine Learning Algorithms: Supervised Learning -- 2.2.4 Machine Learning Algorithms: Unsupervised Learning -- 2.2.4.1 Clustering -- 2.2.4.2 Dimension Reduction -- 2.2.4.3 Anomaly Detection -- 2.2.5 Machine Learning Algorithms That Use Unsupervised Learning -- 2.3 Considerations in Choosing an Algorithm -- 2.4 What Are the Most Common and Popular Machine Learning ; Algorithms? -- 2.4.1 Linear Regression -- 2.4.2 Two Types of Linear Regression -- 2.4.2.1 Simple Linear Regression -- 2.4.2.2 Multiple Linear Regression -- 2.4.2.3 Assumptions of Linear Regression -- 2.4.2.4 Advantages -- 2.4.2.5 Disadvantages -- 2.4.2.6 Sample Python Code for Linear Regression -- 2.4.3 K-Nearest Neighbors (KNN) -- 2.4.3.1 Assumptions.; 2.4.3.2 How Does KNN Algorithm Works? -- 2.4.3.3 Advantages -- Sample Python Code for KNN Algorithm -- 2.4.4 Logistic Regression -- 2.4.4.1 Types of Logistic Regression -- 2.4.4.2 Assumptions -- 2.4.4.3 Advantages -- 2.4.4.4 Disadvantages -- Sample Python Code for Implementing Logistic Regression -- 2.4.5 Naïve Bayes Classifier Algorithm -- 2.4.5.1 Additive Smoothing -- 2.4.5.2 Types of Naïve Bayes Model -- 2.4.5.3 Assumptions -- 2.4.5.4 How Naïve Bayes Algorithm Works? -- 2.4.5.5 Advantages -- 2.4.5.6 Disadvantages -- Sample Python Code for Naïve Bayes Model -- 2.4.6 Support Vector Machine Algorithm -- 2.4.6.1 Assumptions -- 2.4.6.2 Types of SVM -- 2.4.6.3 Advantages -- 2.4.6.4 Disadvantages -- Sample Python Code for SVM -- 2.4.7 Decision Trees -- 2.4.7.1 Information Gain -- 2.4.7.2 Gini Index -- 2.4.7.3 Decision Tree Terminology -- 2.4.7.4 Assumptions -- 2.4.7.5 How Does the Decision Tree Classifier Work? -- 2.4.7.6 Advantages -- 2.4.7.7 Disadvantages -- Sample Python Code for Decision Tree -- 2.4.8 Ensemble Learning -- 2.4.8.1 Types of Ensemble Learning -- Boosting -- Bootstrap Aggregation (Bagging) -- 2.4.9 Random Forests -- 2.4.9.1 Assumptions -- 2.4.9.2 How Does Random Forest Algorithm Work? -- 2.4.9.3 Advantages -- 2.4.9.4 Disadvantages -- Sample Python Code for Random Forest -- 2.4.10 K-Means Clustering Algorithm -- 2.4.10.1 Assumptions -- 2.4.10.2 How Does K-Means Algorithm Work? -- 2.4.10.3 Convergence Criterion -- 2.4.10.4 Advantages -- 2.4.10.5 Disadvantages -- Sample Python Code for K-Means -- 2.4.11 Artificial Neural Networks -- 2.4.11.1 Advantages -- 2.4.11.2 Disadvantages -- 2.5 Usage of ML Algorithms -- 2.6 Performance Metrics of ML Algorithms -- 2.6.1 Testing Data -- 2.6.2 Performance Metrics for Classification Models -- 2.6.2.1 Confusion Matrix -- 2.6.2.2 Regression Metrics -- 2.7 Most Popular Machine Learning Software Tools.; 2.8 Machine Learning Platforms -- 2.8.1 Alteryx Analytics -- 2.8.2 H2O.ai -- 2.8.3 KNIME Analytics Platform -- 2.8.4 RapidMiner -- 2.8.5 Databricks Unified Analytics Platform -- 2.8.6 Microsoft´s Azure Machine Learning Studio -- 2.8.7 Google´s Analytics Platform -- 2.8.8 IBM Watson -- 2.8.9 Amazon Web Services (AWS) -- 2.9 Points to Ponder -- References -- Chapter 3: Deep Learning and Cloud Computing -- 3.1 Deep Learning (DL) -- 3.2 Historical Trends -- 3.3 How Do Deep Learning Algorithm Learn? -- 3.3.1 Activation Functions -- 3.4 Architectures -- 3.4.1 Deep Neural Network (DNN) -- 3.4.2 Recurrent Neural Network (RNN) -- 3.4.3 Convolutional Neural Networks (CNN) -- 3.5 Choosing a Network -- 3.6 Deep Learning Development Flow -- 3.7 What Is Deep About Deep Learning? -- 3.8 Data Used for Deep Learning -- 3.9 Difference Between Machine Learning and Deep Learning -- 3.10 Why Deep Learning Became Popular Now? -- 3.11 Should You Always Use Deep Learning Instead of Machine Learning? -- 3.12 Why Is Deep Learning Important? -- 3.13 What Are the Drawbacks of Deep Learning? -- 3.14 Which Deep Learning Software Frameworks Are Available? -- 3.15 Classical Problems of Deep Learning Solves -- 3.15.1 Image Classification -- 3.15.2 Natural Language Processing -- 3.16 The Future of Deep Learning -- 3.17 Points to Ponder -- References -- Chapter 4: Cloud Computing Concepts -- 4.1 Roots of Cloud Computing -- 4.2 Key Characteristics of Cloud Computing -- 4.3 Various Cloud Stakeholders ; -- 4.4 Pain Points in Cloud Computing -- 4.5 AI and ML in Cloud -- 4.6 Expanding Cloud Reach -- 4.7 Future Trends -- 4.8 Summary -- 4.9 Points to Ponder -- References -- Part II: Practices -- Chapter 5: Practical Aspects in Machine Learning -- 5.1 Preprocessing Data -- 5.2 Challenges in Data Preparation -- 5.3 When to Use Data Preprocessing? -- 5.4 Framework for Data Preparation Techniques.; 5.4.1 Data Preparation -- 5.4.2 Data Selection (Feature Selection) -- 5.4.3 Data Preprocessing -- 5.4.4 Data Cleaning -- 5.4.5 Insufficient Data -- 5.4.6 Non-representative Data -- 5.4.7 Substandard Data -- 5.4.8 Data Transformation -- 5.4.9 Handling Missing Values -- 5.5 Modification of Categorical or Text Values to Numerical Values -- 5.6 Feature Scaling -- 5.6.1 Techniques of Feature Scaling -- 5.6.1.1 Feature Scaling: Standardization -- 5.6.1.2 Feature Scaling: Normalization (Min-Max Normalization) -- 5.7 Inconsistent Values -- 5.8 Duplicated Values -- 5.9 Feature Aggregation -- 5.10 Feature Sampling -- 5.10.1 Sampling Without Replacement -- 5.10.2 Sampling with Replacement -- 5.11 Multicollinearity and Its Impact -- 5.12 Feature Selection -- 5.12.1 Importance of Feature Selection -- 5.12.2 How Many Features to Have in the Model? -- 5.12.3 Types of Feature Selection -- 5.12.3.1 Filter Method -- 5.12.3.2 Wrapper Methods -- 5.12.3.3 Embedded Methods (Fig. 5.7) -- LASSO Regression -- Ridge Regression -- 5.13 Dimensionality Reduction -- 5.13.1 Principal Component Analysis (PCA) -- 5.13.2 Linear Discriminant Analysis -- 5.13.3 t-Distributed Stochastic Neighbor Embedding (t-SNE) -- 5.14 Dealing with Imbalanced Data -- 5.14.1 Use the Right Evaluation Metrics -- 5.14.2 Sampling-Based Approaches -- 5.14.3 Algorithm Based Approach -- 5.15 Points to Ponder -- References -- Chapter 6: Information Security and Cloud Computing -- 6.1 Information Security Background and Context -- 6.2 Privacy Issues -- 6.3 Security Concerns of Cloud Operating Models -- 6.4 Secure Transmissions, Storage, and Computation -- 6.5 A Few Key Challenges Related to Cloud Computing and Virtualization -- 6.6 Security Practices for Cloud Computing -- 6.7 Role of ML for Cybersecurity -- 6.8 Summary -- 6.9 Points to Ponder -- References -- Chapter 7: Examples of Analytics in the Cloud.; 7.1 Background -- 7.2 Analytics Services in the Cloud -- 7.3 Introduction to MapReduce -- 7.4 Introduction to Hadoop -- 7.5 Examples of Cloud-Based ML -- 7.5.1 Cloud Security Monitoring Using AWS -- 7.5.2 Greener Energy Future with ML in GCP -- 7.5.3 Monorail Monitoring in Azure -- 7.5.4 Detecting Online Hate Speech Using NLP -- 7.6 Future Possibilities -- 7.7 Summary -- 7.8 Points to Ponder -- References -- Chapter 8: Health Care in the Cloud: A Few Case Studies -- 8.1 Introduction -- 8.2 Existing TCD Solution -- 8.3 Trail of Bubbles -- 8.4 Moving Data to the Cloud -- 8.5 A Reader in the Cloud -- 8.6 Cloud-Based Collaborative Tools -- 8.7 Multi-Cloud Solutions -- 8.8 UCSD Antibiogram: Using Unclassifiable Data -- 8.9 Next Steps -- 8.10 Summary -- 8.11 Points to Ponder -- References -- Chapter 9: Trends in Hardware-Based AL and ML -- 9.1 Revisiting the History of AI -- 9.2 Current Limitations of AI and ML -- 9.3 Emergence of AI Hardware Accelerators -- 9.3.1 Use of GPUs -- 9.3.2 Use of FPGAs -- 9.3.3 Dedicated AI Accelerators Using ASICs -- 9.4 Cerebras´s Wafer Scale AI Engine -- 9.5 Google Cloud TPUs -- 9.6 Amazon´s Inference Engine -- 9.7 Intel´s Movidius VPU -- 9.8 Apple´s AI Ecosystem -- 9.9 Summary -- 9.10 Points to Ponder -- References -- Appendix A -- AI/ML for App Store Predictions -- Using Python for App Metrics Predictions in Google Play Store -- Step 1: Obtain Data -- Step 2: Scrub and Prepare the Data -- Step 2.1: Null Value Removal/Fills -- Step 2.2: Duplicate Removal -- Exact Duplicates (876) -- Slight Variations (294) -- Step 2.3: Data Type Conversions -- Step 2.4: Dummy Variables -- Step 2.5: ; Rescale -- Step 3: Data Exploration -- Step 4: Categorization -- Step 4.1: Google App Ratings -- Step 4.2: Google App Reviews -- Step 4.3: Google App Sizes -- Step 4. 4: Google App Installs -- Step 4.5: Type -- Step 4.6: Price.; Step 4.7: Content Rating.

The memoirs of Lady Hyegyŏng : the autobiographical writings of a crown princess of eighteenth-century Korea / / Lady Hyegyŏng ; translated with an introduction and annotations by JaHyun Kim Haboush ; with a new foreword by Dorothy Ko

• Record Nr.: UNINA9910810034203321
• Autore: Hyegyŏng, Lady
• Pubbl/distr/stampa: Berkeley : , : University of California Press, , [2013]; ©2013
• ISBN: 0-520-95729-6
• Descrizione fisica: 1 online resource (400 p.)
• Altri autori (Persone): HaboushJaHyun Kim; KoDorothy
• Disciplina: 951.9/02/092 B
• Soggetti: Civilization, Modern - 18th century; Princesses - Korea; Korea Kings and rulers; Korea History 1637-1864
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Frontmatter -- Contents -- Foreword -- Acknowledgments -- Introduction -- Translator's Note -- Principal Persons -- The Memoirs of Lady Hyegyong -- Appendix 1: Genealogical Table of the Yi Royal House -- Appendix 2: Genealogical Table of the Hong Family -- Appendix 3: Genealogical Table of the Kyongju Kim Family -- Notes -- Glossary -- Index
• Sommario/riassunto: Lady Hyegyong's memoirs, which recount the chilling murder of her husband by his father, form one of the best known and most popular classics of Korean literature. From 1795 until 1805 Lady Hyegyong composed this masterpiece, depicting a court life Shakespearean in its ; pathos, drama, and grandeur. Presented in its social, cultural, and historical contexts, this first complete English translation opens a door into a world teeming with conflicting passions, political intrigue, and the daily preoccupations of a deeply intelligent and articulate woman. JaHyun Kim Haboush's accurate, fluid translation captures the intimate and expressive voice of this consummate storyteller. Reissued nearly twenty years after its initial publication with a new foreword by Dorothy Ko, The Memoirs of Lady Hyegyong is a unique exploration of Korean selfhood and an extraordinary example of autobiography in the premodern era.