Entropy Randomization In Machine Learning

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Entropy Randomization in Machine Learning

Yuri S. Popkov,Alexey Yu. Popkov,Yuri A. Dubnov
Entropy Randomization in Machine Learning Book PDF

Author : Yuri S. Popkov,Alexey Yu. Popkov,Yuri A. Dubnov
Publisher : CRC Press
Page : 463 pages
File Size : 51,7 Mb
Release : 2022-08-09
Category : Computers
ISBN : 9781000628739

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Entropy Randomization in Machine Learning by Yuri S. Popkov,Alexey Yu. Popkov,Yuri A. Dubnov Pdf

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

Mark Liu
Machine Learning Animated Book PDF

Author : Mark Liu
Publisher : CRC Press
Page : 465 pages
File Size : 50,8 Mb
Release : 2023-10-30
Category : Computers
ISBN : 9781000964776

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Machine Learning, Animated by Mark Liu Pdf

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

Uday Kamath,Kenneth Graham,Wael Emara
Transformers for Machine Learning Book PDF

Author : Uday Kamath,Kenneth Graham,Wael Emara
Publisher : CRC Press
Page : 284 pages
File Size : 54,6 Mb
Release : 2022-05-24
Category : Computers
ISBN : 9781000587074

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Transformers for Machine Learning by Uday Kamath,Kenneth Graham,Wael Emara Pdf

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

Marco Scutari,Mauro Malvestio
The Pragmatic Programmer for Machine Learning Book PDF

Author : Marco Scutari,Mauro Malvestio
Publisher : CRC Press
Page : 357 pages
File Size : 48,6 Mb
Release : 2023-03-31
Category : Computers
ISBN : 9781000852462

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The Pragmatic Programmer for Machine Learning by Marco Scutari,Mauro Malvestio Pdf

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

Mark Stamp
Introduction to Machine Learning with Applications in Information Security Book PDF

Author : Mark Stamp
Publisher : CRC Press
Page : 498 pages
File Size : 53,5 Mb
Release : 2022-09-27
Category : Business & Economics
ISBN : 9781000626261

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Introduction to Machine Learning with Applications in Information Security by Mark Stamp Pdf

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

Ronald T. Kneusel
The Art of Randomness Book PDF

Author : Ronald T. Kneusel
Publisher : No Starch Press
Page : 402 pages
File Size : 41,5 Mb
Release : 2024-03-05
Category : Computers
ISBN : 9781718503250

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The Art of Randomness by Ronald T. Kneusel Pdf

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

Shlomo Dubnov,Ross Greer
Deep and Shallow Book PDF

Author : Shlomo Dubnov,Ross Greer
Publisher : CRC Press
Page : 345 pages
File Size : 50,5 Mb
Release : 2023-12-08
Category : Computers
ISBN : 9781000984477

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Deep and Shallow by Shlomo Dubnov,Ross Greer Pdf

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

The Cross-Entropy Method

Reuven Y. Rubinstein,Dirk P. Kroese
The Cross Entropy Method Book PDF

Author : Reuven Y. Rubinstein,Dirk P. Kroese
Publisher : Springer Science & Business Media
Page : 316 pages
File Size : 52,7 Mb
Release : 2013-03-09
Category : Computers
ISBN : 9781475743210

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The Cross-Entropy Method by Reuven Y. Rubinstein,Dirk P. Kroese Pdf

Rubinstein is the pioneer of the well-known score function and cross-entropy methods. Accessible to a broad audience of engineers, computer scientists, mathematicians, statisticians and in general anyone, theorist and practitioner, who is interested in smart simulation, fast optimization, learning algorithms, and image processing.

Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough

Vinit Kumar Gunjan,Jacek M. Zurada,Balasubramanian Raman,G. R. Gangadharan
Modern Approaches in Machine Learning and Cognitive Science A Walkthrough Book PDF

Author : Vinit Kumar Gunjan,Jacek M. Zurada,Balasubramanian Raman,G. R. Gangadharan
Publisher : Springer Nature
Page : 243 pages
File Size : 55,5 Mb
Release : 2020-02-04
Category : Technology & Engineering
ISBN : 9783030384456

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Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough by Vinit Kumar Gunjan,Jacek M. Zurada,Balasubramanian Raman,G. R. Gangadharan Pdf

This book discusses various machine learning & cognitive science approaches, presenting high-throughput research by experts in this area. Bringing together machine learning, cognitive science and other aspects of artificial intelligence to help provide a roadmap for future research on intelligent systems, the book is a valuable reference resource for students, researchers and industry practitioners wanting to keep abreast of recent developments in this dynamic, exciting and profitable research field. It is intended for postgraduate students, researchers, scholars and developers who are interested in machine learning and cognitive research, and is also suitable for senior undergraduate courses in related topics. Further, it is useful for practitioners dealing with advanced data processing, applied mathematicians, developers of software for agent-oriented systems and developers of embedded and real-time systems.

Minimum Error Entropy Classification

Joaquim P. Marques de Sá,Luís M.A. Silva,Jorge M.F. Santos,Luís A. Alexandre
Minimum Error Entropy Classification Book PDF

Author : Joaquim P. Marques de Sá,Luís M.A. Silva,Jorge M.F. Santos,Luís A. Alexandre
Publisher : Springer
Page : 262 pages
File Size : 46,8 Mb
Release : 2012-07-25
Category : Technology & Engineering
ISBN : 9783642290299

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Minimum Error Entropy Classification by Joaquim P. Marques de Sá,Luís M.A. Silva,Jorge M.F. Santos,Luís A. Alexandre Pdf

This book explains the minimum error entropy (MEE) concept applied to data classification machines. Theoretical results on the inner workings of the MEE concept, in its application to solving a variety of classification problems, are presented in the wider realm of risk functionals. Researchers and practitioners also find in the book a detailed presentation of practical data classifiers using MEE. These include multi‐layer perceptrons, recurrent neural networks, complexvalued neural networks, modular neural networks, and decision trees. A clustering algorithm using a MEE‐like concept is also presented. Examples, tests, evaluation experiments and comparison with similar machines using classic approaches, complement the descriptions.

Information Theoretic Learning

Jose C. Principe
Information Theoretic Learning Book PDF

Author : Jose C. Principe
Publisher : Springer Science & Business Media
Page : 538 pages
File Size : 54,9 Mb
Release : 2010-04-06
Category : Computers
ISBN : 9781441915702

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Information Theoretic Learning by Jose C. Principe Pdf

This book is the first cohesive treatment of ITL algorithms to adapt linear or nonlinear learning machines both in supervised and unsupervised paradigms. It compares the performance of ITL algorithms with the second order counterparts in many applications.

Data Mining and Machine Learning in Cybersecurity

Sumeet Dua,Xian Du
Data Mining and Machine Learning in Cybersecurity Book PDF

Author : Sumeet Dua,Xian Du
Publisher : CRC Press
Page : 275 pages
File Size : 54,9 Mb
Release : 2011-04-25
Category : Computers
ISBN : 9781466508231

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Data Mining and Machine Learning in Cybersecurity by Sumeet Dua,Xian Du Pdf

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

Chu Kiong Loo,Yap Keem Siah,Kok Wai Wong,Andrew Teoh Beng Jin,Kaizhu Huang
Neural Information Processing Book PDF

Author : Chu Kiong Loo,Yap Keem Siah,Kok Wai Wong,Andrew Teoh Beng Jin,Kaizhu Huang
Publisher : Springer
Page : 706 pages
File Size : 50,9 Mb
Release : 2014-10-21
Category : Computers
ISBN : 9783319126432

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Neural Information Processing by Chu Kiong Loo,Yap Keem Siah,Kok Wai Wong,Andrew Teoh Beng Jin,Kaizhu Huang Pdf

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.

Learning Theory

John Shawe-Taylor,Yoram Singer
Learning Theory Book PDF

Author : John Shawe-Taylor,Yoram Singer
Publisher : Springer
Page : 654 pages
File Size : 47,6 Mb
Release : 2004-06-11
Category : Computers
ISBN : 9783540278191

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Learning Theory by John Shawe-Taylor,Yoram Singer Pdf

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.

Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques

Moses Charikar,Klaus Jansen,Omer Reingold,José D.P. Rolim
Approximation Randomization and Combinatorial Optimization Algorithms and Techniques Book PDF

Author : Moses Charikar,Klaus Jansen,Omer Reingold,José D.P. Rolim
Publisher : Springer
Page : 628 pages
File Size : 48,6 Mb
Release : 2007-08-28
Category : Computers
ISBN : 9783540742081

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Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques by Moses Charikar,Klaus Jansen,Omer Reingold,José D.P. Rolim Pdf

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.