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Entropy Randomization in Machine Learning
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, Ent...
Deep and Shallow
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
Introduction to Machine Learning with Applications in Information Security
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...
The Pragmatic Programmer for Machine Learning
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.
Transformers for Machine Learning
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 visi...
Machine Learning, Animated
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
Beyond Memory
Photography possesses a powerful ability to bear witness, aid remembrance, shape, and even alter recollection. In Beyond Memory: Soviet Nonconformist Photography and Photo-Related Works of Art, the general editor, Diane Neumaier, and twenty-three contributors offer a rigorous examination of the medium's role in late Soviet unofficial art. Focusing on the period between the mid-1950s and the late 1980s, they explore artists' unusually inventive and resourceful uses of photography within a highly developed Soviet dissident culture. During this time, lack of high-quality photographic materials, complimented by tremendous creative impulses, prompted artists to explore experimental photo-processe...
Networks in the Russian Market Economy
A PDF version of this book is available for free in open access via the OAPEN Library platform, www.oapen.org. This book examines the significance of networks among the firms operative in the contemporary Russian software industry in the St. Petersburg region.
Heterogeneous Photocatalysis
The book explains the principles and fundamentals of photocatalysis and highlights the current developments and future potential of the green-chemistry-oriented applications of various inorganic, organic, and hybrid photocatalysts. The book consists of eleven chapters, including the principles and fundamentals of heterogeneous photocatalysis; the mechanisms and dynamics of surface photocatalysis; research on TiO2-based composites with unique nanostructures; the latest developments and advances in exploiting photocatalyst alternatives to TiO2; and photocatalytic materials for applications other than the traditional degradation of pollutants, such as carbon dioxide reduction, water oxidation, ...
Mathematical Reviews
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