Entropy Randomization In Machine Learning

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Entropy Randomization in Machine Learning

Author : Yuri S. Popkov
Publisher : CRC Press
ISBN 13 : 1000628736
Total Pages : 463 pages
Book Rating : 4.0/5 (6 download)

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Book Synopsis Entropy Randomization in Machine Learning by : Yuri S. Popkov

Download or read book Entropy Randomization in Machine Learning written by Yuri S. Popkov and published by CRC Press. This book was released on 2022-08-09 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: Entropy Randomization in Machine Learning presents a new approach to machine learning—entropy randomization—to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). Randomized machine-learning procedures involve models with random parameters and maximum entropy estimates of the probability density functions of the model parameters under balance conditions with measured data. Optimality conditions are derived in the form of nonlinear equations with integral components. A new numerical random search method is developed for solving these equations in a probabilistic sense. Along with the theoretical foundations of randomized machine learning, Entropy Randomization in Machine Learning considers several applications to binary classification, modelling the dynamics of the Earth’s population, predicting seasonal electric load fluctuations of power supply systems, and forecasting the thermokarst lakes area in Western Siberia. Features • A systematic presentation of the randomized machine-learning problem: from data processing, through structuring randomized models and algorithmic procedure, to the solution of applications-relevant problems in different fields • Provides new numerical methods for random global optimization and computation of multidimensional integrals • A universal algorithm for randomized machine learning This book will appeal to undergraduates and postgraduates specializing in artificial intelligence and machine learning, researchers and engineers involved in the development of applied machine learning systems, and researchers of forecasting problems in various fields.

Machine Learning, Animated

Author : Mark Liu
Publisher : CRC Press
ISBN 13 : 1000964779
Total Pages : 465 pages
Book Rating : 4.0/5 (9 download)

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Book Synopsis Machine Learning, Animated by : Mark Liu

Download or read book Machine Learning, Animated written by Mark Liu and published by CRC Press. This book was released on 2023-10-30 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: The release of ChatGPT has kicked off an arms race in Machine Learning (ML), however ML has also been described as a black box and very hard to understand. Machine Learning, Animated eases you into basic ML concepts and summarizes the learning process in three words: initialize, adjust and repeat. This is illustrated step by step with animation to show how machines learn: from initial parameter values to adjusting each step, to the final converged parameters and predictions. This book teaches readers to create their own neural networks with dense and convolutional layers, and use them to make binary and multi-category classifications. Readers will learn how to build deep learning game strategies and combine this with reinforcement learning, witnessing AI achieve super-human performance in Atari games such as Breakout, Space Invaders, Seaquest and Beam Rider. Written in a clear and concise style, illustrated with animations and images, this book is particularly appealing to readers with no background in computer science, mathematics or statistics. Access the book's repository at: https://github.com/markhliu/MLA

Transformers for Machine Learning

Author : Uday Kamath
Publisher : CRC Press
ISBN 13 : 100058707X
Total Pages : 284 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Transformers for Machine Learning by : Uday Kamath

Download or read book Transformers for Machine Learning written by Uday Kamath and published by CRC Press. This book was released on 2022-05-24 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time Series, and Computer Vision. Transformers have gone through many adaptations and alterations, resulting in newer techniques and methods. Transformers for Machine Learning: A Deep Dive is the first comprehensive book on transformers. Key Features: A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers. 60+ transformer architectures covered in a comprehensive manner. A book for understanding how to apply the transformer techniques in speech, text, time series, and computer vision. Practical tips and tricks for each architecture and how to use it in the real world. Hands-on case studies and code snippets for theory and practical real-world analysis using the tools and libraries, all ready to run in Google Colab. The theoretical explanations of the state-of-the-art transformer architectures will appeal to postgraduate students and researchers (academic and industry) as it will provide a single entry point with deep discussions of a quickly moving field. The practical hands-on case studies and code will appeal to undergraduate students, practitioners, and professionals as it allows for quick experimentation and lowers the barrier to entry into the field.

The Pragmatic Programmer for Machine Learning

Author : Marco Scutari
Publisher : CRC Press
ISBN 13 : 1000852466
Total Pages : 357 pages
Book Rating : 4.0/5 (8 download)

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Book Synopsis The Pragmatic Programmer for Machine Learning by : Marco Scutari

Download or read book The Pragmatic Programmer for Machine Learning written by Marco Scutari and published by CRC Press. This book was released on 2023-03-31 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has redefined the way we work with data and is increasingly becoming an indispensable part of everyday life. The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions discusses how modern software engineering practices are part of this revolution both conceptually and in practical applictions. Comprising a broad overview of how to design machine learning pipelines as well as the state-of-the-art tools we use to make them, this book provides a multi-disciplinary view of how traditional software engineering can be adapted to and integrated with the workflows of domain experts and probabilistic models. From choosing the right hardware to designing effective pipelines architectures and adopting software development best practices, this guide will appeal to machine learning and data science specialists, whilst also laying out key high-level principlesin a way that is approachable for students of computer science and aspiring programmers.

Introduction to Machine Learning with Applications in Information Security

Author : Mark Stamp
Publisher : CRC Press
ISBN 13 : 1000626261
Total Pages : 498 pages
Book Rating : 4.0/5 (6 download)

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Book Synopsis Introduction to Machine Learning with Applications in Information Security by : Mark Stamp

Download or read book Introduction to Machine Learning with Applications in Information Security written by Mark Stamp and published by CRC Press. This book was released on 2022-09-27 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn’t prove theorems, or dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth, including Hidden Markov Models (HMM), Support Vector Machines (SVM), and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN), boosting, Random Forests, and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation, Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented, including Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), Extreme Learning Machines (ELM), Residual Networks (ResNet), Deep Belief Networks (DBN), Bidirectional Encoder Representations from Transformers (BERT), and Word2Vec. Finally, several cutting-edge deep learning topics are discussed, including dropout regularization, attention, explainability, and adversarial attacks. Most of the examples in the book are drawn from the field of information security, with many of the machine learning and deep learning applications focused on malware. The applications presented serve to demystify the topics by illustrating the use of various learning techniques in straightforward scenarios. Some of the exercises in this book require programming, and elementary computing concepts are assumed in a few of the application sections. However, anyone with a modest amount of computing experience should have no trouble with this aspect of the book. Instructor resources, including PowerPoint slides, lecture videos, and other relevant material are provided on an accompanying website: http://www.cs.sjsu.edu/~stamp/ML/.

The Art of Randomness

Author : Ronald T. Kneusel
Publisher : No Starch Press
ISBN 13 : 1718503253
Total Pages : 402 pages
Book Rating : 4.7/5 (185 download)

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Book Synopsis The Art of Randomness by : Ronald T. Kneusel

Download or read book The Art of Randomness written by Ronald T. Kneusel and published by No Starch Press. This book was released on 2024-03-05 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Harness the power of randomness (and Python code) to solve real-world problems in fun, hands-on experiments—from simulating evolution to encrypting messages to making machine-learning algorithms! The Art of Randomness is a hands-on guide to mastering the many ways you can use randomized algorithms to solve real programming and scientific problems. You’ll learn how to use randomness to run simulations, hide information, design experiments, and even create art and music. All you need is some Python, basic high school math, and a roll of the dice. Author Ronald T. Kneusel focuses on helping you build your intuition so that you’ll know when and how to use random processes to get things done. You’ll develop a randomness engine (a Python class that supplies random values from your chosen source), then explore how to leverage randomness to: Simulate Darwinian evolution and optimize with swarm-based search algorithms Design scientific experiments to produce more meaningful results by making them truly random Implement machine learning algorithms like neural networks and random forests Use Markov Chain Monte Carlo methods to sample from complex distributions Hide information in audio files and images, generate art, and create music Reconstruct original signals and images from only randomly sampled data Scientific anecdotes and code examples throughout illustrate how randomness plays into areas like optimization, machine learning, and audio signals. End-of-chapter exercises encourage further exploration. Whether you’re a programmer, scientist, engineer, mathematician, or artist, you’ll find The Art of Randomness to be your ticket to discovering the hidden power of applied randomness and the ways it can transform your approach to solving problems, from the technical to the artistic.

Deep and Shallow

Author : Shlomo Dubnov
Publisher : CRC Press
ISBN 13 : 1000984478
Total Pages : 345 pages
Book Rating : 4.0/5 (9 download)

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Book Synopsis Deep and Shallow by : Shlomo Dubnov

Download or read book Deep and Shallow written by Shlomo Dubnov and published by CRC Press. This book was released on 2023-12-08 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a holistic overview of the foundational ideas in music, from the physical and mathematical properties of sound to symbolic representations Combines signlas and language models in one place to explore how sound may be represented and manipulated by computer systems More complex discussions are gradually incorporated and each chapter includes guided programming activities to familiarise readers with the discussed theory

Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques

Author : Moses Charikar
Publisher : Springer
ISBN 13 : 3540742085
Total Pages : 636 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques by : Moses Charikar

Download or read book Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques written by Moses Charikar and published by Springer. This book was released on 2007-08-28 with total page 636 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents the refereed proceedings of the 10th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems and the 11th International Workshop on Randomization and Computation. The papers cover design and analysis of approximation algorithms, hardness of approximation, small space and data streaming algorithms, sub-linear time algorithms, embeddings and metric space methods, and much more.

Randomness

Author : Source Wikipedia
Publisher : University-Press.org
ISBN 13 : 9781230583075
Total Pages : 108 pages
Book Rating : 4.5/5 (83 download)

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Book Synopsis Randomness by : Source Wikipedia

Download or read book Randomness written by Source Wikipedia and published by University-Press.org. This book was released on 2013-09 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 106. Chapters: Random variable, Entropy, Shuffling, Dice, Determinism, Monte Carlo method, Random sequence, Hardware random number generator, Randomization, Infinite monkey theorem, Randomizer, Two-stage model of free will, Biology Monte Carlo method, Fisher-Yates shuffle, Indeterminacy, History of randomness, Algorithmically random sequence, Random number generation, Algorithmic information theory, Shuffling machine, Physical Unclonable Function, Randomness extractor, Differential entropy, Applications of randomness, Random password generator, Global Consciousness Project, Edward Kofler, Random binary tree, /dev/random, Philosophical interpretation of classical physics, Clock drift, Subrandom numbers, Nothing up my sleeve number, Linear partial information, Entropy estimation, Mendelian randomization, Monte Carlo algorithm, Randomness tests, Yao's principle, Impossibility of a gambling system, Seven states of randomness, Control variates, Random number table, Las Vegas algorithm, A Million Random Digits with 100,000 Normal Deviates, Random stimulus, Diceware, Random permutation, Random compact set, Diehard tests, Random variate, Random sample, Lavarand, LavaRnd, Spinner, Shuffle play.

Data Mining and Machine Learning in Cybersecurity

Author : Sumeet Dua
Publisher : CRC Press
ISBN 13 : 146650823X
Total Pages : 275 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Data Mining and Machine Learning in Cybersecurity by : Sumeet Dua

Download or read book Data Mining and Machine Learning in Cybersecurity written by Sumeet Dua and published by CRC Press. This book was released on 2011-04-25 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role in cybersecurity. Although several conferences, workshops, and journals focus on the fragmented research topics in this area, there has been no single interdisciplinary resource on past and current works and possible paths for future research in this area. This book fills this need. From basic concepts in machine learning and data mining to advanced problems in the machine learning domain, Data Mining and Machine Learning in Cybersecurity provides a unified reference for specific machine learning solutions to cybersecurity problems. It supplies a foundation in cybersecurity fundamentals and surveys contemporary challenges—detailing cutting-edge machine learning and data mining techniques. It also: Unveils cutting-edge techniques for detecting new attacks Contains in-depth discussions of machine learning solutions to detection problems Categorizes methods for detecting, scanning, and profiling intrusions and anomalies Surveys contemporary cybersecurity problems and unveils state-of-the-art machine learning and data mining solutions Details privacy-preserving data mining methods This interdisciplinary resource includes technique review tables that allow for speedy access to common cybersecurity problems and associated data mining methods. Numerous illustrative figures help readers visualize the workflow of complex techniques and more than forty case studies provide a clear understanding of the design and application of data mining and machine learning techniques in cybersecurity.

Neural Information Processing

Author : Chu Kiong Loo
Publisher : Springer
ISBN 13 : 3319126431
Total Pages : 723 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Neural Information Processing by : Chu Kiong Loo

Download or read book Neural Information Processing written by Chu Kiong Loo and published by Springer. This book was released on 2014-10-21 with total page 723 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three volume set LNCS 8834, LNCS 8835, and LNCS 8836 constitutes the proceedings of the 20th International Conference on Neural Information Processing, ICONIP 2014, held in Kuching, Malaysia, in November 2014. The 231 full papers presented were carefully reviewed and selected from 375 submissions. The selected papers cover major topics of theoretical research, empirical study, and applications of neural information processing research. The 3 volumes represent topical sections containing articles on cognitive science, neural networks and learning systems, theory and design, applications, kernel and statistical methods, evolutionary computation and hybrid intelligent systems, signal and image processing, and special sessions intelligent systems for supporting decision, making processes, theories and applications, cognitive robotics, and learning systems for social network and web mining.

Entropy in Real-World Datasets and Its Impact on Machine Learning

Author : Jan Kozak
Publisher : Mdpi AG
ISBN 13 : 9783036578484
Total Pages : 0 pages
Book Rating : 4.5/5 (784 download)

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Book Synopsis Entropy in Real-World Datasets and Its Impact on Machine Learning by : Jan Kozak

Download or read book Entropy in Real-World Datasets and Its Impact on Machine Learning written by Jan Kozak and published by Mdpi AG. This book was released on 2023-06-14 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The topic of the reprint is very important nowadays, because ever-evolving machine learning techniques make it possible to obtain better real-world data. Therefore, this reprint contains information related to real data in fields such as automatic sign language translation, bike-sharing travel characteristics, stock index, sports data, fake news data, and more. However, it should be noted that the reprint also contains a lot of information on new developments in machine learning, new algorithms, algorithm modifications, and a new measure of classification quality assessment that also takes into account the preferences of the decision maker.

Learning Theory

Author : John Shawe-Taylor
Publisher : Springer
ISBN 13 : 3540278192
Total Pages : 656 pages
Book Rating : 4.5/5 (42 download)

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Book Synopsis Learning Theory by : John Shawe-Taylor

Download or read book Learning Theory written by John Shawe-Taylor and published by Springer. This book was released on 2004-06-11 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 17th Annual Conference on Learning Theory, COLT 2004, held in Banff, Canada in July 2004. The 46 revised full papers presented were carefully reviewed and selected from a total of 113 submissions. The papers are organized in topical sections on economics and game theory, online learning, inductive inference, probabilistic models, Boolean function learning, empirical processes, MDL, generalisation, clustering and distributed learning, boosting, kernels and probabilities, kernels and kernel matrices, and open problems.

Entropy in Real-World Datasets and Its Impact on Machine Learning

Author : Przemyslaw Juszczuk
Publisher :
ISBN 13 : 9783036578491
Total Pages : 0 pages
Book Rating : 4.5/5 (784 download)

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Book Synopsis Entropy in Real-World Datasets and Its Impact on Machine Learning by : Przemyslaw Juszczuk

Download or read book Entropy in Real-World Datasets and Its Impact on Machine Learning written by Przemyslaw Juszczuk and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The topic of the reprint is very important nowadays, because ever-evolving machine learning techniques make it possible to obtain better real-world data. Therefore, this reprint contains information related to real data in fields such as automatic sign language translation, bike-sharing travel characteristics, stock index, sports data, fake news data, and more. However, it should be noted that the reprint also contains a lot of information on new developments in machine learning, new algorithms, algorithm modifications, and a new measure of classification quality assessment that also takes into account the preferences of the decision maker.

Applications and Techniques in Information Security

Author : Srikanth Prabhu
Publisher : Springer Nature
ISBN 13 : 981992264X
Total Pages : 311 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Applications and Techniques in Information Security by : Srikanth Prabhu

Download or read book Applications and Techniques in Information Security written by Srikanth Prabhu and published by Springer Nature. This book was released on 2023-05-11 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th International Conference, on Applications and Techniques in Information Security, ATIS 2022, held in Manipal, India, December 30–31, 2022. The 23 full papers included in this book were carefully reviewed and selected from 121 submissions. They were organized in topical sections as follows: Privacy and Security, Advances in Machine Learning, Cloud, IoT and Computing Technologies.

Adversarial Machine Learning

Author : Aneesh Sreevallabh Chivukula
Publisher : Springer Nature
ISBN 13 : 3030997723
Total Pages : 316 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Adversarial Machine Learning by : Aneesh Sreevallabh Chivukula

Download or read book Adversarial Machine Learning written by Aneesh Sreevallabh Chivukula and published by Springer Nature. This book was released on 2023-03-06 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: A critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed. We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications. In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.

Explainable AI (XAI) for Sustainable Development

Author : Lakshmi D
Publisher : CRC Press
ISBN 13 : 1040038832
Total Pages : 335 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Explainable AI (XAI) for Sustainable Development by : Lakshmi D

Download or read book Explainable AI (XAI) for Sustainable Development written by Lakshmi D and published by CRC Press. This book was released on 2024-06-26 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents innovative research works to automate, innovate, design, and deploy AI fo real-world applications. It discusses AI applications in major cutting-edge technologies and details about deployment solutions for different applications for sustainable development. The application of Blockchain techniques illustrates the ways of optimisation algorithms in this book. The challenges associated with AI deployment are also discussed in detail, and edge computing with machine learning solutions is explained. This book provides multi-domain applications of AI to the readers to help find innovative methods towards the business, sustainability, and customer outreach paradigms in the AI domain. • Focuses on virtual machine placement and migration techniques for cloud data centres • Presents the role of machine learning and meta-heuristic approaches for optimisation in cloud computing services • Includes application of placement techniques for quality of service, performance, and reliability improvement • Explores data centre resource management, load balancing and orchestration using machine learning techniques • Analyses dynamic and scalable resource scheduling with a focus on resource management The reference work is for postgraduate students, professionals, and academic researchers in computer science and information technology.