Search Results - (((((((ant OR cantee) OR wanting) OR wkanted) OR cantor) OR anne) OR start) OR partes) algorithms.

Deep Learning with Pytorch Quick Start Guide : Learn to Train and Deploy Neural Network Models in Python. by Julian, David

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Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5 by Pastor Sanz, Iván

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Condition Monitoring with Vibration Signals : Compressive Sampling and Learning Algorithms for Rotating Machines. by Nandi, Asoke K.

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Vehicular networks : models and algorithms

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Getting Started with Greenplum for Big Data Analytics. by Gollapudi, Sunila

Table of Contents: “…Intro -- Getting Started with Greenplum for Big Data Analytics -- Table of Contents -- Getting Started with Greenplum for Big Data Analytics -- Credits -- Foreword -- About the Author -- Acknowledgement -- About the Reviewers -- www.PacktPub.com -- Support files, eBooks, discount offers and more -- Why Subscribe? …”
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Wireless Communications : Algorithmic Techniques. by Vitetta, Giorgio

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Biomedical signal analysis : contemporary methods and applications by Theis, Fabian J.

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Parts-feeding systems for assembly : organisation, logistics and automation

Table of Contents: “…Cover; Editorial; Kanban-driven parts feeding within a semiautomated O-shaped assembly line: a case study in the automotive industry; Re-balancing problem for assembly lines: new mathematical model and exact solution method; A Changeover Time Reduction through an integration of lean practices: a case study from pharmaceutical sector; Planning models for continuous supply of parts in assembly systems; A decision model for kitting and line stocking with variable operator walking distances; Advances in assembly line parts feeding policies: a literature review.…”
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Machine Learning with Scikit-Learn Quick Start Guide : Classification, Regression, and Clustering Techniques in Python. by Jolly, Kevin

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A guide to creating a successful algorithmic trading strategy by Kaufman, Perry J.

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Relational data clustering : models, algorithms, and applications

Table of Contents: “…Front cover; Contents; List of Tables; List of Figures; Preface; Chapter 1: Introduction; Part I: Models; Chapter 2: Co-Clustering; Chapter 3: Heterogeneous Relational Data Clustering; Chapter 4: Homogeneous Relational Data Clustering; Chapter 5: General Relational Data Clustering; Chapter 6: Multiple-View Relational Data Clustering; Chapter 7: Evolutionary Data Clustering; Part II: Algorithms; Chapter 8: Co-Clustering; Chapter 9: Heterogeneous Relational Data Clustering; Chapter 10: Homogeneous Relational Data Clustering; Chapter 11: General Relational Data Clustering.…”
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TensorFlow Reinforcement Learning Quick Start Guide : Get up and Running with Training and Deploying Intelligent, Self-Learning Agents Using Python. by Balakrishnan, Kaushik

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Handbook of Satisfiability : Handbook of Satisfiability. by Biere, A.

Table of Contents: “…Title page; Contents; Part I. Theory and Algorithms; Chapter 1. A History of Satisfiability; 1.1 Preface: the concept of satisfiability; 1.2 The ancients; 1.3 The medieval period; 1.4 The renaissance; 1.5 The first logic machine; 1.6 Boolean algebra; 1.7 Frege, logicism, and quantification logic; 1.8 Russell and Whitehead; 1.9 G""odel's incompleteness theorem; 1.10 Effective process and recursive functions; 1.11 Herbrand's theorem; 1.12 Model theory and Satisfiability; 1.13 Completeness of first-order logic; 1.14 Application of logic to circuits; 1.15 Resolution.…”
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Discrete Algebraic Methods : Arithmetic, Cryptography, Automata and Groups

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Stochastic processes in polymeric fluids : tools and examples for developing simulation algorithms by Öttinger, Hans Christian, 1958-

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Recent developments in computational finance : foundations, algorithms and applications

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Natural Language Processing with Python Quick Start Guide : Going from a Python Developer to an Effective Natural Language Processing Engineer. by Kasliwal, Nirant

Table of Contents: “…; Runtime complexity; Cleaning a corpus with FlashText; Summary; Chapter 3: Leveraging Linguistics; Linguistics and NLP; Getting started; Introducing textacy; Redacting names with named entity recognition; Entity types; Automatic question generation; Part-of-speech tagging; Creating a ruleset; Question and answer generation using dependency parsing; Visualizing the relationship; Introducing textacy; Leveling up -- question and answer; Putting it together and the end; Summary…”
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Machine Learning with Apache Spark Quick Start Guide : Uncover Patterns, Derive Actionable Insights, and Learn from Big Data Using MLlib. by Quddus, Jillur

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Hands-on recommendation systems with Python : start building powerful and personalized, recommendation engines with Python by Banik, Rounak

Table of Contents: “…Computing the cosine similarity scoreBuilding the recommender function; Metadata-based recommender; Preparing the data; The keywords and credits datasets; Wrangling keywords, cast, and crew; Creating the metadata soup; Generating the recommendations; Suggestions for improvements; Summary; Chapter 5: Getting Started with Data Mining Techniques; Problem statement; Similarity measures; Euclidean distance; Pearson correlation; Cosine similarity ; Clustering; k-means clustering; Choosing k; Other clustering algorithms; Dimensionality reduction; Principal component analysis.…”
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JPEG2000 standard for image compression : concepts, algorithms and VLSI architectures by Acharya, Tinku

Table of Contents: “…Preface; 1 Introduction to Data Compression; 2 Source Coding Algorithms; 3 JPEG: Still Image Compression Standard; 4 Introduction to Discrete Wavelet Transform; 5 VLSI Architectures for Discrete Wavelet Transforms; 6 JPEG2000 Standard; 7 Coding Algorithms in JPEG2000; 8 Code-Stream Organization and File Format; 9 VLSI Architectures for JPEG2000; 10 Beyond Part 1 of JPEG2000 Standard; Index; About the Authors.…”
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