Search Results - (((((((kant OR want) OR mantis) OR when) OR cantor) OR anne) OR share) OR hints) algorithms.

Architecture-aware optimization strategies in real-time image processing by Li, Chao, Balla-Arabe, Souleymane, Yang, Fan

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Deep learning with Python : a hands-on introduction by Ketkar, Nikhil

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Robust and error-free geometric computing by Eberly, Dave

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R High Performance Programming. by Lim, Aloysius

Table of Contents: “…Data parallelism versus task parallelismImplementing data parallel algorithms; Implementing task parallel algorithms; Running the same task on workers in a cluster; Running different tasks on workers in a cluster; Executing tasks in parallel on a cluster of computers; Shared memory versus distributed memory parallelism; Optimizing parallel performance; Summary; Chapter 9: Offloading Data Processing to Database Systems; Extracting data into R versus processing data in a database; Preprocessing data in a relational database using SQL; Converting R expressions into SQL; Using dplyr…”
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Features and processing in agreement by Mancini, Simona, 1978-

Table of Contents: “…1st/2nd vs. 3rd person: Person underspecification and context-dependencePronoun representation and interpretive anchors; The featural makeup of pronouns; Summary; Chapter Five; When disagreement is grammatical: Unagreement; Unagreement processing and the role of interpretive anchors; Unagreeing, null and overt subjects; Summary; Chapter Six; From feature bundles to feature an; Representations, algorithms and neuroanatomical bases of agreement; Relation to existing sentence comprehension models; Conclusion; Notes; Bibliography; Index…”
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Regression Analysis : Theory, Methods, and Applications by Sen, Ashish

Table of Contents: “…Random Variables -- B.1.2 Correlated Random Variables -- B.1.3 Sample Statistics -- B.1.4 Linear Combinations of Random Variables -- B.2 Random Vectors -- B.3 The Multivariate Normal Distribution -- B.4 The Chi-Square Distributions -- B.5 The F and t Distributions -- B.6 Jacobian of Transformations -- B.7 Multiple Correlation -- Problems -- C Nonlinear Least Squares -- C.1 Gauss-Newton Type Algorithms -- C.1.1 The Gauss-Newton Procedure -- C.1.2 Step Halving -- C.1.3 Starting Values and Derivatives -- C.1.4 Marquardt Procedure -- C.2 Some Other Algorithms -- C.2.1 Steepest Descent Method -- C.2.2 Quasi-Newton Algorithms -- C.2.3 The Simplex Method -- C.2.4 Weighting -- C.3 Pitfalls -- C.4 Bias, Confidence Regions and Measures of Fit -- C.5 Examples -- Problems -- Tables -- References -- Author Index.…”
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Machine Learning for Asset Management New Developments and Financial Applications

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Hands-On Reinforcement Learning with Python : Master Reinforcement and Deep Reinforcement Learning Using OpenAI Gym and TensorFlow. by Ravichandiran, Sudharsan

Table of Contents: “…Solving the taxi problem using Q learningSARSA; Solving the taxi problem using SARSA; The difference between Q learning and SARSA; Summary; Questions; Further reading; Chapter 6: Multi-Armed Bandit Problem; The MAB problem; The epsilon-greedy policy; The softmax exploration algorithm; The upper confidence bound algorithm; The Thompson sampling algorithm; Applications of MAB; Identifying the right advertisement banner using MAB; Contextual bandits; Summary; Questions; Further reading; Chapter 7: Deep Learning Fundamentals; Artificial neurons; ANNs; Input layer; Hidden layer; Output layer.…”
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Cryptography 101 : From Theory to Practice. by Oppliger, Rolf

Table of Contents: “…12.2.3 Asymmetric Encryption-Based Key Distribution Protocol -- 12.3 KEY AGREEMENT -- 12.4 QUANTUM CRYPTOGRAPHY -- 12.4.1 Basic Principles -- 12.4.2 Quantum Key Exchange Protocol -- 12.4.3 Historical and Recent Developments -- 12.5 FINAL REMARKS -- References -- Chapter 13 Asymmetric Encryption -- 13.1 INTRODUCTION -- 13.2 PROBABILISTIC ENCRYPTION -- 13.2.1 Algorithms -- 13.2.2 Assessment -- 13.3 ASYMMETRIC ENCRYPTION SYSTEMS -- 13.3.1 RSA -- 13.3.2 Rabin -- 13.3.3 Elgamal -- 13.3.4 Cramer-Shoup -- 13.4 IDENTITY-BASED ENCRYPTION -- 13.5 FULLY HOMOMORPHIC ENCRYPTION -- 13.6 FINAL REMARKS -- References -- Chapter 14 Digital Signatures -- 14.1 INTRODUCTION -- 14.2 DIGITAL SIGNATURE SYSTEMS -- 14.2.1 RSA -- 14.2.2 PSS and PSS-R -- 14.2.3 Rabin -- 14.2.4 Elgamal -- 14.2.5 Schnorr -- 14.2.6 DSA -- 14.2.7 ECDSA -- 14.2.8 Cramer-Shoup -- 14.3 IDENTITY-BASED SIGNATURES -- 14.4 ONE-TIME SIGNATURES -- 14.5 VARIANTS -- 14.5.1 Blind Signatures -- 14.5.2 Undeniable Signatures -- 14.5.3 Fail-Stop Signatures -- 14.5.4 Group Signatures -- 14.6 FINAL REMARKS -- References -- Chapter 15 Zero-Knowledge Proofs of Knowledge -- 15.1 INTRODUCTION -- 15.2 ZERO-KNOWLEDGE AUTHENTICATION PROTOCOLS -- 15.2.1 Fiat-Shamir -- 15.2.2 Guillou-Quisquater -- 15.2.3 Schnorr -- 15.3 NONINTERACTIVE ZERO-KNOWLEDGE -- 15.4 FINAL REMARKS -- References -- Part IV CONCLUSIONS -- Chapter 16 Key Management -- 16.1 INTRODUCTION -- 16.1.1 Key Generation -- 16.1.2 Key Distribution -- 16.1.3 Key Storage -- 16.1.4 Key Destruction -- 16.2 SECRET SHARING -- 16.2.1 Shamir's System -- 16.2.2 Blakley's System -- 16.2.3 Verifiable Secret Sharing -- 16.2.4 Visual Cryptography -- 16.3 KEY RECOVERY -- 16.4 CERTIFICATE MANAGEMENT -- 16.4.1 Introduction -- 16.4.2 X.509 Certificates -- 16.4.3 OpenPGP Certificates -- 16.4.4 State of the Art -- 16.5 FINAL REMARKS -- References -- Chapter 17 Summary.…”
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Hands-On Automated Machine Learning : a beginner's guide to building automated machine learning systems using AutoML and Python. by Das, Sibanjan

Table of Contents: “…; Why use AutoML and how does it help?; When do you automate ML?; What will you learn?; Core components of AutoML systems; Automated feature preprocessing; Automated algorithm selection; Hyperparameter optimization; Building prototype subsystems for each component; Putting it all together as an end-to-end AutoML system; Overview of AutoML libraries; Featuretools; Auto-sklearn; MLBox; TPOT; Summary.…”
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Helping Children Learn Mathematics.

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Bladder Cancer : diagnosis and clinical management

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OpenCV Android Programming By Example. by Muhammad, Amgad

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Apache Spark for Data Science Cookbook. by Chitturi, Padma Priya

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Social sensing : building reliable systems on unreliable data by Wang, Dong, Abdelzaher, Tarek, Kaplan, Lance

Table of Contents: “…Kaplan; Foreword; Preface; Chapter 1: A new information age; 1.1 Overview; 1.2 Challenges; 1.3 State of the Art; 1.3.1 Efforts on Discount Fusion; 1.3.2 Efforts on Trust and Reputation Systems; 1.3.3 Efforts on Fact-Finding; 1.4 Organization; Chapter 2: Social Sensing Trends and Applications; 2.1 Information Sharing: The Paradigm Shift; 2.2 An Application Taxonomy; 2.3 Early Research; 2.4 The Present Time.…”
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Two-degree-of-freedom control systems : the Youla Parameterization approach by Keviczky, László, Bányász, Csilla

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Constraint Solving over Multi-valued Logics : Application to Digital Circuits.

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MATLAB for Machine Learning. by Ciaburro, Giuseppe

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Statistical learning from a regression perspective by Berk, Richard A.

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Computing the homology of the lambda algebra by Tangora, Martin C.

Table of Contents: “…Corollaries to the structure formulas""; ""2.6. Tri-grading when p is odd""; ""2.7. The endomorphism Î?""; ""2.8. …”
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