03506nam 2200637Ia 450 991046279760332120211005154705.01-4081-9482-11-4081-6477-9(CKB)2670000000361276(MiAaPQ)EBC3003281(MiAaPQ)EBC1164289(MiAaPQ)EBC1187323(Au-PeEL)EBL3003281(CaPaEBR)ebr10682520(CaONFJC)MIL603939(OCoLC)836400716(MiAaPQ)EBC6158679(Au-PeEL)EBL1164289(Au-PeEL)EBL6158679(OCoLC)1151184829(EXLCZ)99267000000036127620090314d2009 uy 0engurcn|||||||||txtrdacontentcrdamediacrrdacarrierThe UFO files[electronic resource] the inside story of real-life sightings /[compiled by] David Clarke1st ed.Kew, Richmond, Surrey, U.K. National Archives2009160 p. ill"Largely based upon the real-life accounts of UFO experiences recorded in files collected by Britain's Ministry of Defence (MoD), many of which are today held by the National Archives"--p. [7].1-4081-6489-2 Includes bibliographical references (p. [152]-155) and index.Cover -- Contents -- Acknowledgements -- Introduction -- 1 Strange Lights in the Skies -- UFO FILE: Did Churchill order a UFO cover-up? -- 2 The Flying Saucer Age -- UFO FILE: 'Saucer Sam' -- 3 Cold War UFOs -- UFO FILE: All Balloony? -- 4 Close Encounters -- UFO FILE: A Visit from the Men in Black -- 5 Crop Circles and Alien Abductions -- UFO FILE: The Missing Files -- 6 Turn-of-the-Century UFOs -- UFO FILE: UFOs on Film -- 7 Closing the UFO Files -- UFO FILE: Their last Word on UFOs -- Afterword -- Appendix -- Notes and references -- Resources -- Index.In this remarkable book, fully updated for this second edition, David Clarke reveals an array of startling stories from possible UFO reports hidden among Met Office investigations of aerial phenomena in the 1920s to the conclusions of Project Condign, the secret British Intelligence UFO study completed in 2000.As well as covering Roswell and Britain's own Rendlesham Forest mystery, Clarke raids the records for dramatic stories of abductions and close encounters, ghost aircraft and crop circles, and UFO reports by both civilian aircrew and military personnel. Dramatic witness statements and interviews combine with rarely seen photographs, drawings and newly available documents to offer a unique guide to one of our most intriguing mysteries.Unidentified flying objectsSightings and encountersUnidentified flying objectsSightings and encountersHistoryCrop circlesGreat BritainAlien abductionGreat BritainElectronic books.Unidentified flying objectsSightings and encounters.Unidentified flying objectsSightings and encountersHistory.Crop circlesAlien abduction001.942Clarke David1967-1041500National Archives (Great Britain)MiAaPQMiAaPQMiAaPQBOOK9910462797603321The UFO files2465050UNINA10701nam 2200481 450 991048398630332120211211141832.03-030-71270-2(CKB)4100000011912130(MiAaPQ)EBC6578036(Au-PeEL)EBL6578036(OCoLC)1249473159(PPN)25529610X(EXLCZ)99410000001191213020211211d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierIntroduction to machine learning in the cloud with Python concepts and practices /Pramod Gupta, Naresh K. SehgalCham, Switzerland :Springer,[2021]©20211 online resource (293 pages)3-030-71269-9 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.Machine learningMachine learning.006.31Gupta Pramod1069649Sehgal Naresh KumarMiAaPQMiAaPQMiAaPQBOOK9910483986303321Introduction to machine learning in the cloud with Python2556485UNINA03461nam 22006975 450 991029862210332120250609110826.03-319-15494-X10.1007/978-3-319-15494-7(CKB)3710000000402731(EBL)2094394(SSID)ssj0001500952(PQKBManifestationID)11918301(PQKBTitleCode)TC0001500952(PQKBWorkID)11519752(PQKB)10951210(DE-He213)978-3-319-15494-7(MiAaPQ)EBC2094394(PPN)185485375(MiAaPQ)EBC3109903(EXLCZ)99371000000040273120150417d2015 u| 0engur|n|---|||||txtccrPolymers on the Crime Scene Forensic Analysis of Polymeric Trace Evidence /by Valerio Causin1st ed. 2015.Cham :Springer International Publishing :Imprint: Springer,2015.1 online resource (364 p.)Description based upon print version of record.3-319-15493-1 Includes bibliographical references and index.Introduction -- Polymers: an overview -- Polymeric traces: transfer, persistence, recovery, analysis and interpretation of analytical data -- Polymers on the crime scene -- Formulation: polymer matrix, fillers, dyes, pigments and other additives -- Synthesis-dependent parameters: molecularweight, constitution and configuration -- Processing-dependent parameters: structure and morphology of polymeric materials.This book approaches the analysis of forensic contact traces from a polymer science perspective. The development of characterization methods of new or unusual traces and the improvement of existing protocols is described.The book starts with a general introduction to polymers and  the issues related to transfer, persistence and recovery of polymeric traces. The chapters  present a distinctive feature of polymers, discussing how it can be measured, what the practical difficulties which can be encountered in the analysis, and how useful that information is for comparison or identification purposes. Practical tips for the realization of the forensic analyses are included.PolymersForensic sciencesBiomedical materialsMedical jurisprudencePolymer Scienceshttps://scigraph.springernature.com/ontologies/product-market-codes/C22008Forensic Sciencehttps://scigraph.springernature.com/ontologies/product-market-codes/B23000Biomaterialshttps://scigraph.springernature.com/ontologies/product-market-codes/Z13000Forensic Medicinehttps://scigraph.springernature.com/ontologies/product-market-codes/H23007Polymers.Forensic sciences.Biomedical materials.Medical jurisprudence.Polymer Sciences.Forensic Science.Biomaterials.Forensic Medicine.54541.2254614.1620.11Causin Valerioauthttp://id.loc.gov/vocabulary/relators/aut925444BOOK9910298622103321Polymers on the Crime Scene2077944UNINA