Search Results - (((((((ant OR arts) OR vian) OR date) OR cantor) OR anne) OR halted) OR wanting) algorithms.

F♯ for Machine Learning Essentials. by Mukherjee, Sudipta

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Introduction to network security : theory and practice by Wang, Jie, 1961-

Table of Contents: “…Front Matter -- Network Security Overview -- Data Encryption Algorithms -- Public-Key Cryptography and Key Management -- Data Authentication -- Network Security Protocols in Practice -- Wireless Network Security -- Cloud Security -- Network Perimeter Security -- Intrusion Detections -- The Art of Anti-Malicious Software -- 7-bit ASCII code -- SHA-512 Constants (in Hexadecimal) -- Data Compression Using ZIP -- Base64 Encoding -- Cracking WEP Keys Using WEPCrack -- Acronyms -- Further Readings -- Index.…”
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Inside jokes : using humor to reverse-engineer the mind by Hurley, Matthew M., 1977-

Table of Contents: “…The phenomenology of humor -- Humor as a property of objects or events -- Duchenne laughter -- The systematic ineffability of humor -- Funny-ha-ha and funny-huh -- The knowledge-relativity of humor -- Mating and dating.…”
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Advances in digital technologies : proceedings of the 6th International Conference on Applications of Digital Information and Web Technologies 2015

Table of Contents: “…Application of Genetic Algorithms to Context-Sensitive Text MiningA Decision Tree Classification Model for Determining the Location for Solar Power Plant; A Framework for Multi-Label Learning Using Label Ranking and Correlation; A Comparative Analysis of Pruning Methods for C4.5 and Fuzzy C4.5; Subject Index; Author Index.…”
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Machine Learning for Mobile : Practical Guide to Building Intelligent Mobile Applications Powered by Machine Learning. by Gopalakrishnan, Revathi

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Java Deep Learning Projects : Implement 10 Real-World Deep Learning Applications Using Deeplearning4j and Open Source APIs. by Karim, Rezaul

Table of Contents: “…; Artificial Neural Networks; Biological neurons; A brief history of ANNs; How does an ANN learn?; ANNs and the backpropagation algorithm; Forward and backward passes; Weights and biases; Weight optimization; Activation functions.…”
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Behavioral formation : volatile design processes & the emergence of a strange specificity by Snooks, Roland

Table of Contents: “…Intro -- ACKNOWLEDGMENTS -- PREFACE -- INTRODUCTION -- BEHAVIORAL METHODOLOGIES -- FIBROUS ASSEMBLAGES -- FIBROUS TOWER -- POLYSCALAR TECTONICS -- MULTI-AGENT ALGORITHMS -- AGENTBODIES -- BABIY YAR MEMORIAL -- MANIFOLD SWARMS -- RMIT MACE -- BEHAVIORAL COMPOSITES -- COMPOSITE SWARM -- COMPOSITE WING -- RMIT MEETING PAVILION -- AGENTBODY PROTOTYPES -- NGV PAVILION -- STRIATED POLYMERS -- VOLATILITY -- AGENCY OF STRUCTURE -- STRANGE FEEDBACK -- YEOSU PAVILION -- NATIONAL ART MUSEUM OF CHINA -- STRANGE SPECIFICITY -- PAINTERLY FORMATION -- STIGMERGIC ROBOTICS -- CATALOG OF PROJECTS -- GLOSSARY OF TERMS -- BIBLIOGRAPHY.…”
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Optimization advances in electric power systems

Table of Contents: “…Unreliability Costs -- 3.5. Proposed Algorithms -- 3.5.1. ES and TS Algorithms -- 3.5.2. …”
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Implementing cryptography using Python by Bray, Shannon, 1972-

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Artificial intelligence for big data : complete guide to automating big data solutions using artificial intelligence techniques. by Deshpande, Anand

Table of Contents: “…Snowball stemming -- Lancaster stemming -- Lovins stemming -- Dawson stemming -- Lemmatization -- N-grams -- Feature extraction -- One hot encoding -- TF-IDF -- CountVectorizer -- Word2Vec -- CBOW -- Skip-Gram model -- Applying NLP techniques -- Text classification -- Introduction to Naive Bayes' algorithm -- Random Forest -- Naive Bayes' text classification code example -- Implementing sentiment analysis -- Frequently asked questions -- Summary -- Chapter 7: Fuzzy Systems -- Fuzzy logic fundamentals -- Fuzzy sets and membership functions -- Attributes and notations of crisp sets -- Operations on crisp sets -- Properties of crisp sets -- Fuzzification -- Defuzzification -- Defuzzification methods -- Fuzzy inference -- ANFIS network -- Adaptive network -- ANFIS architecture and hybrid learning algorithm -- Fuzzy C-means clustering -- NEFCLASS -- Frequently asked questions -- Summary -- Chapter 8: Genetic Programming -- Genetic algorithms structure -- KEEL framework -- Encog machine learning framework -- Encog development environment setup -- Encog API structure -- Introduction to the Weka framework -- Weka Explorer features -- Preprocess -- Classify -- Attribute search with genetic algorithms in Weka -- Frequently asked questions -- Summary -- Chapter 9: Swarm Intelligence -- Swarm intelligence -- Self-organization -- Stigmergy -- Division of labor -- Advantages of collective intelligent systems -- Design principles for developing SI systems -- The particle swarm optimization model -- PSO implementation considerations -- Ant colony optimization model -- MASON Library -- MASON Layered Architecture -- Opt4J library -- Applications in big data analytics -- Handling dynamical data -- Multi-objective optimization -- Frequently asked questions -- Summary -- Chapter 10: Reinforcement Learning -- Reinforcement learning algorithms concept.…”
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Digital governance of education : technology, standards and Europeanization of education by Landri, Paolo

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Data clustering in C++ : an object-oriented approach by Gan, Guojun, 1979-

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Debates in the digital humanities 2016

Table of Contents: “…The Differences Between Digital Humanities and Digital History / Stephen Robertson -- Digital History Perpetual Future Tense / Cameron Blevins -- Collections and/of Data: Art History and the Art Museum in the DH Mode / Matthew Battles and Michael Maizels -- Archaeology, the Digital Humanities, and the "Big Tent" / Ethan Watrall ; Navigating the Global Digital Humanities: Insights from Black Feminism / Roopika Risam. …”
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Machine Learning with Core ML : an IOS Developer's Guide to Implementing Machine Learning in Mobile Apps. by Newnham, Joshua

Table of Contents: “…Learning algorithms Auto insurance in Sweden; Supported learning algorithms; Considerations ; Summary; Chapter 3: Recognizing Objects in the World; Understanding images; Recognizing objects in the world; Capturing data ; Preprocessing the data; Performing inference ; Summary ; Chapter 4: Emotion Detection with CNNs; Facial expressions; Input data and preprocessing ; Bringing it all together; Summary ; Chapter 5: Locating Objects in the World; Object localization and object detection ; Converting Keras Tiny YOLO to Core ML; Making it easier to find photos; Optimizing with batches; Summary.…”
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Microbiological sensors for the drinking water industry

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Function Estimates. by Marron, J. S.

Table of Contents: “…Partial spline modelling of the tropopause and other discontinuitiesChoice of smoothing parameter in deconvolution problems -- Regression approximation using projections and isotropic kernels -- Will the art of smoothing ever become a science?…”
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Mastering machine learning with scikit-learn : learn to implement and evaluate machine learning solutions with scikit-learn by Hackeling, Gavin

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Handbook of foot and ankle orthopedics by Shah, Rajiv

Table of Contents: “…-- Plantar heel pain -- Growing with flat feet: childhood to adulthood -- Lucid approach and simplistic management of diabetic foot -- Common foot and ankle infections: diagnosis and management -- Art of arthrodesis in foot and ankle -- Dealing with tendo achilles problems! …”
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Arithmetic and logic in computer systems by Lu, Mi

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Advances in time series forecasting. Volume 2

Table of Contents: “…INTRODUCTION -- CLASSICAL TIME SERIES FORECASTING MODELS -- ARTIFICIAL NEURAL NETWORKS FOR FORECASTING TIME SERIES -- A NEW ARTIFICIAL NEURAL NETWORK WITH DETERMINISTIC COMPONENTS -- APPLICATIONS -- CONCLUSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- A Fuzzy Time Series Approach Based on Genetic Algorithm with Single Analysis Process -- Ozge Cagcag Yolcu* -- INTRODUCTION -- FUZZY TIME SERIES -- RELATED METHODS -- Genetic Algorithm (GA) -- Single Multiplicative Neuron Model -- PROPOSED METHOD -- APPLICATIONS -- CONCLUSION AND DISCUSSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- Forecasting Stock Exchanges with Fuzzy Time Series Approach Based on Markov Chain Transition Matrix -- Cagdas Hakan Aladag1,* and Hilal Guney2 -- INTRODUCTION -- FUZZY TIME SERIES -- TSAUR 'S FUZZY TIME SERIES MARKOV CHAIN MODEL -- THE IMPLEMENTATION -- CONCLUSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- A New High Order Multivariate Fuzzy Time Series Forecasting Model -- Ufuk Yolcu* -- INTRODUCTION -- RELATED METHODOLOGY -- The Fuzzy C-Means (FCM) Clustering Method -- Single Multiplicative Neuron Model Artificial Neural Network (SMN-ANN) -- Fuzzy Time Series -- THE PROPOSED METHOD -- APPLICATIONS -- CONCLUSIONS AND DISCUSSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- Fuzzy Functions Approach for Time Series Forecasting -- Ali Z. …”
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