Search Results - (((((((kant OR kkantis) OR wants) OR hints) OR cantor) OR anne) OR file) OR wanting) algorithms.

Computer science : reflections on the field, reflections from the field

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Direct numerical simulations of gas-liquid multiphase flows by Tryggvason, Grétar, Scardovelli, Ruben, Zaleski, S.

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Ivor Horton's beginning Visual C++ 2012 by Horton, Ivor

Table of Contents: “…Precompiled Header Files --…”
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Integrated systems engineering : a postprint volume from the IFAC Conference, Baden-Baden, Germany, 27-29 September 1994

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Satellite networking : principles and protocols by Sun, Zhili

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Modeling Reality : How Computers Mirror Life. by Białynicki-Birula, Iwo

Table of Contents: “…Contents; 1 From building blocks to computers: Models and modeling; 2 The game of life: A legendary cellular automaton; 3 Heads or tails: Probability of an event; 4 Galton's board: Probability and statistics; 5 Twenty questions: Probability and information; 6 Snowflakes: The evolution of dynamical systems; 7 The Lorenz butterfly: Deterministic chaos; 8 From Cantor to Mandelbrot: Self-similarity and fractals; 9 Typing monkeys: Statistical linguistics; 10 The bridges of Königsberg: Graph theory; 11 Prisoner's dilemma: Game theory; 12 Let the best man win: Genetic algorithms.…”
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Advanced dynamic-system simulation : model -replication and Monte Carlo Studies by Korn, Granino A. (Granino Arthur), 1922-2013

Table of Contents: “…Classifier Input from Files --…”
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Media technologies : essays on communication, materiality, and society

Table of Contents: “…Bowker -- "What Do We Want?" "Materiality!" "When Do We Want It?" "Now!" …”
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Handbook of performability engineering

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

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Comprehensive Ruby Programming. by Hudgens, Jordan

Table of Contents: “…-- A better way -- SOLID OOP development -- the Liskov substitution principle -- The LSP definition -- Breaking down the LSP -- The LSP example -- The problem -- The LSP violation -- The fix -- SOLID OOP development -- the interface segregation principle -- The ISP definition -- The ISP code example -- Introducing the moderator -- A better way -- The result -- A caveat -- SOLID OOP development -- the dependency inversion principle -- The DIP in the real world -- The DIP definition -- The DIP code example -- Recap -- Summary -- Chapter 10: Working with the Filesystem in Ruby -- Creating a file -- Ruby File class -- Other options you can pass as the second option -- Reading files into a program using the File class -- Deleting a file -- Appending a file.…”
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What intelligence tests miss : the psychology of rational thought by Stanovich, Keith E., 1950-

Table of Contents: “…Inside George W. Bush's mind : hints at what IQ tests miss -- Dysrationalia : separating rationality and intelligence -- The reflective mind, the algorithmic mind, and the autonomous mind -- Cutting intelligence down to size -- Why intelligent people doing foolish things is no surprise -- The cognitive miser : ways to avoid thinking -- Framing and the cognitive miser -- Myside processing : heads I win, tails I win too! …”
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Pattern discovery in biomolecular data : tools, techniques, and applications

Table of Contents: “…Discovering patterns in DNA sequences by the algorithmic significance method / Aleksandar Milosavljevic -- Assembling blocks / Jorja G. …”
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Artificial neural systems : principle and practice by Lorrentz, Pierre

Table of Contents: “…INTRODUCTIONDENSITY BASED ALGORITHMS: CLUSTERING ALGORITHMS; NATURE-BASED ALGORITHMS; Evolutionary Algorithm and Programming ; Genetic Algorithm; GA Operators; APPLICATIONS OF GENETIC ALGORITHM; NETWORK METHOD: EDGES AND NODES; MULTI-LAYERED PERCEPTRON; REAL-TIME APPLICATIONS OF STATE-OF-THE-ART ANN SYSTEMS; DEFINITION OF ARTIFICIAL NEURAL NETWORKS (ANN); Intelligence; An Artificial Neural Network (ANN) system; PERFORMANCE MEASURES; Receiver's Operating Characteristics (ROC); Hypothesis Testing; Chi-squared (Goodness-of-fit) Test; CONFLICT OF INTEREST; ACKNOWLEDGEMENTS; REFERENCES…”
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Machine Learning for Mobile : Practical Guide to Building Intelligent Mobile Applications Powered by Machine Learning. by Gopalakrishnan, Revathi

Table of Contents: “…Decision tree Advantages of the decision tree algorithm; Disadvantages of decision trees; Advantages of decision trees; Random forests; Solving the problem using random forest in Core ML; Dataset; Naming the dataset; Technical requirements; Creating the model file using scikit-learn ; Converting the scikit model to the Core ML model; Creating an iOS mobile application using the Core ML model; Summary; Further reading; Chapter 4: TensorFlow Mobile in Android; An introduction to TensorFlow; TensorFlow Lite components; Model-file format; Interpreter; Ops/Kernel…”
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Hands-On Artificial Intelligence for IoT : Expert Machine Learning and Deep Learning Techniques for Developing Smarter IoT Systems. by Kapoor, Amita

Table of Contents: “…Using TXT files in PythonCSV format; Working with CSV files with the csv module; Working with CSV files with the pandas module; Working with CSV files with the NumPy module; XLSX format; Using OpenPyXl for XLSX files; Using pandas with XLSX files; Working with the JSON format; Using JSON files with the JSON module; JSON files with the pandas module; HDF5 format; Using HDF5 with PyTables; Using HDF5 with pandas; Using HDF5 with h5py; SQL data; The SQLite database engine; The MySQL database engine; NoSQL data; HDFS; Using hdfs3 with HDFS; Using PyArrow's filesystem interface for HDFS; Summary…”
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Basic data analysis for time series with R by Derryberry, DeWayne R.

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Navigation signal processing for GNSS software receivers by Pany, Thomas

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Machine learning : a Bayesian and optimization perspective by Theodoridis, Sergios, 1951-

Table of Contents: “…Probability and stochastic processes -- Learning in parametric modeling: basic concepts and directions -- Mean-square error linear estimation -- Stochastic gradient descent: the LMS algorithm -- The least-squares family -- Classification: a tour of the classics -- Parameter learning: a convex analytic path -- Sparsity-aware learning: concepts and theoretical foundations -- Sparcity-aware learning: algorithms and applications -- Learning in reproducing Kernel Hilbert spaces -- Bayesian learning: inference and the EM alogrithm -- Bayesian learning: approximate inference and nonparametric models -- Monte Carlo methods -- Probabilistic graphical models: Part I -- Probabilistic graphical models: Part II -- Particle filtering -- Neural networks and deep learning -- Dimensionality reduction -- Appendix A LInear algebra -- Appendix B Probability theory and statistics -- Appendix C Hints on constrained optimization.…”
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F♯ for Machine Learning Essentials. by Mukherjee, Sudipta

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