Название: Knowledge Recommendation Systems with Machine Intelligence Algorithms
Автор: Jarosław Protasiewicz
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2023
Страниц: 139
Язык: английский
Формат: pdf (true), epub
Размер: 13.1 MB

Knowledge recommendation is an urgent and timely topic encountered in research and information services. There is a strongly compelling and urgent need: the modern economy badly requires highly skilled professionals, researchers, and innovators, which enables opportunities to gain competitive advantages and assist in managing financial resources and available goods, as well as carrying out fundamental and applied research more effectively. The design, development, and implementation of the two representative IT systems discussed in the book supplemented with content-based recommendation algorithms illustrate how the paradigm and theory of knowledge recommendation work in practice. This also includes a way of the development and practical application of selected heuristics and Machine Learning/machine intelligence algorithms that aim to create individuals’ expertise profiles and to deliver ways of evaluating enterprise innovation. The book contains an original material and is unique in many ways. The prudent and though-out selection and the exposure of the topics, depth of coverage of the subject matter, and original insights are the focal features of the book. New and promising directions and techniques of Machine Learning applied to knowledge recommendation are original.

Название: Machine Learning in Python for Dynamic Process Systems: A practitioner’s guide for building process modeling, predictive, and monitoring solutions using dynamic data
Автор: Ankur Kumar, Jesus Flores-Cerrillo
Издательство: Leanpub
Год: June 2023
Страниц: 208
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

This book provides a comprehensive coverage of Machine Learning (ML) methods that have proven useful in process industry for dynamic process modeling. Step-by-step instructions, supported with industry-relevant case studies, show (using Python) how to develop solutions for process modeling, process monitoring, etc., using classical and modern methods. This book is designed to help readers gain a working-level knowledge of machine learning-based dynamic process modeling techniques that have proven useful in process industry. Readers can leverage the concepts learned to build advanced solutions for process monitoring, soft sensing, inferential modeling, predictive maintenance, and process control for dynamic systems. The application-focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers, and data scientists. No prior experience with Machine Learning or Python is needed. Undergraduate-level knowledge of basic linear algebra and calculus is assumed.

Название: Attention Augmented Learning Machines: Theory and Applications
Автор: Guoqiang Zhong, Jinxuan Sun
Издательство: Nova Science Publishers, Inc.
Год: 2023
Страниц: 140
Язык: английский
Формат: pdf (true)
Размер: 35.6 MB

Deep Learning has developed for more than 10 years. Many novel models are proposed. Among others, the attention models have greatly impacted the Deep Learning area. Similar to the attention mechanism of human beings, the attention mechanism improves the performance of many Deep Learning models based on its discovery of important information hidden in data and motivates the emergence of many new Deep Learning models, like Transformer and its variants. This book includes eight chapters and aims to introduce some interesting works on the attention mechanism. Chapter 1 is a review of the attention mechanism used in the Deep Learning area, while Chapters 2 and 3 present two models that integrate the attention mechanism into gated recurrent units (GRUs) and long short-term memory (LSTM), respectively, making them pay attention to important information in the sequences. This book can be used by college students (undergraduate or graduate) chosen to major in Computer Science, Artificial Intelligence, electrical engineering, and mathematics, or others who study or have the potential to use Deep Learning algorithms. It could be of special interest to professors who research pattern recognition, Machine Learning, computer vision, neural language processing (NLP), and related fields, or engineers who apply Deep Learning models to their products. On the other hand, the reader is assumed to be already familiar with basic computer programming, Machine Learning, pattern recognition, and computer vision.

Название: Gradient Expectations: Structure, Origins, and Synthesis of Predictive Neural Networks
Автор: Keith L. Downing
Издательство: The MIT Press
Год: 2023
Страниц: 224
Язык: английский
Формат: epub (true)
Размер: 15.4 MB

An insightful investigation into the mechanisms underlying the predictive functions of neural networks—and their ability to chart a new path for AI. Prediction is a cognitive advantage like few others, inherently linked to our ability to survive and thrive. Our brains are awash in signals that embody prediction. Can we extend this capability more explicitly into synthetic neural networks to improve the function of AI and enhance its place in our world? Gradient Expectations is a bold effort by Keith L. Downing to map the origins and anatomy of natural and artificial neural networks to explore how, when designed as predictive modules, their components might serve as the basis for the simulated evolution of advanced neural network systems. Downing delves into the known neural architecture of the mammalian brain to illuminate the structure of predictive networks and determine more precisely how the ability to predict might have evolved from more primitive neural circuits. He then surveys past and present computational neural models that leverage predictive mechanisms with biological plausibility, identifying elements, such as gradients, that natural and artificial networks share. Behind well-founded predictions lie gradients, Downing finds, but of a different scope than those that belong to today’s Deep Learning.

Название: Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing: Hardware Architectures
Автор: Sudeep Pasricha, Muhammad Shafique
Издательство: Springer
Год: 2024
Страниц: 418
Язык: английский
Формат: pdf (true)
Размер: 21.8 MB

This book presents recent advances towards the goal of enabling efficient implementation of Machine Learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying Machine Learning to innovative application domains, exploring the efficient hardware design of efficient Machine Learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Machine Learning (ML) has emerged as a prominent approach for achieving state-of-the-art accuracy for many data analytic applications, ranging from computer vision (e.g., classification, segmentation, and object detection in images and video), speech recognition, language translation, healthcare diagnostics, robotics, and autonomous vehicles to business and financial analysis. The driving force of the ML success is the advent of neural network (NN) algorithms, such as deep neural networks (DNNs)/Deep Learning (DL) and spiking neural networks (SNNs) with support from today’s evolving computing landscape to better exploit data and thread-level parallelism with ML accelerators. This volume of the book focuses on addressing these challenges from a hardware perspective, with multiple solutions towards the design of efficient accelerators, memory, and emerging technology substrates for embedded ML systems.

Название: Deep Learning: Theory, Architectures and Applications in Speech, Image and Language Processing
Автор: Gyanendra Verma, Rajesh Doriya
Издательство: Bentham Books
Год: 2023
Страниц: 270
Язык: английский
Формат: pdf (true), epub
Размер: 37.6 MB

This book is a detailed reference guide on Deep Learning and its applications. It aims to provide a basic understanding of Deep Learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by Computer Science academics and researchers. By the end of the book, the reader will become familiar with different Deep Learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries. Machine Learning proved its usefulness in many applications in the domain of Image Processing and Computer Vision, Medical Imaging, Satellite imaging, Remote Sensing, Surveillance, etc ., over the past decade. At the same time, Machine Learning methods themselves have evolved, particularly Deep Learning methods that have demonstrated significant performance over traditional Machine Learning algorithms.

Название: Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications
Автор: Abhishek Majumder, Joy Lal Sarkar, Arindam Majumder
Издательство: Bentham Books
Год: 2023
Страниц: 319
Язык: английский
Формат: pdf (true), epub
Размер: 45.8 MB

Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications captures the state of the art in usage of artificial intelligence in different types of recommendation systems and predictive analysis. The book provides guidelines and case studies for application of artificial intelligence in recommendation from expert researchers and practitioners. A detailed analysis of the relevant theoretical and practical aspects, current trends and future directions is presented. A recommendation System is an intelligent computer-based system that serves as a guide and suggests, as per the preferences of the person. It uses state-of-the-art technologies like Big Data, Machine Learning, Artificial Intelligence, etc., and benefits both the consumer and the merchant. Recommendation System is becoming very popular as it serves as a guide for the activity that a person or a group plans to perform in the best possible manner, given the constraints imposed by the user(s). Software tools and techniques provide advice on items to be used by a user.

Название: Numerical Machine Learning
Автор: Zhiyuan Wang, Sayed Ameenuddin Irfan, Christopher Teoh
Издательство: Bentham Books
Год: 2023
Страниц: 225
Язык: английский
Формат: pdf (true), epub
Размер: 32.3 MB

Numerical Machine Learning is a simple textbook on machine learning that bridges the gap between mathematics theory and practice. The book uses numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of seven commonly used machine learning algorithms and techniques, including linear regression, regularization, logistic regression, decision trees, gradient boosting, Support Vector Machine, and K-means Clustering. Through a step-by-step exploration of concrete numerical examples, the students (primarily undergraduate and graduate students studying machine learning) can develop a well-rounded understanding of these algorithms, gain an in-depth knowledge of how the mathematics relates to the implementation and performance of the algorithms, and be better equipped to apply them to practical problems. From our experiences of teaching Machine Learning using various textbooks, we have noticed that there tends to be a strong emphasis on abstract mathematics when discussing the theories of Machine Learning algorithms. On the other hand, in the application of Machine Learning, it usually straightaway goes to import offthe- shelf libraries such as scikit-learn, TensorFlow, Keras, and PyTorch.

Название: Applications of Optimization and Machine Learning in Image Processing and IoT
Автор: Nidhi Gupta
Издательство: CRC Press
Год: 2024
Страниц: 236
Язык: английский
Формат: pdf (true)
Размер: 10.8 MB

This book presents state-of-the-art optimization algorithms followed by Internet of Things (IoT) fundamentals. The applications of machine learning and IoT are explored, with topics including optimization, algorithms and Machine Learning in image processing and IoT. Applications of Optimization and Machine Learning in Image Processing and IoT is a complete reference source, providing the latest research findings and solutions for optimization and machine learning algorithms. The chapters examine and discuss the fields of Machine Learning, IoT and image processing. The field of Computer Science with the fastest growth right now is Machine Learning, which has applications in fields as varied as marketing, health-care, production, cybersecurity and mobility. Three elements are readily available and combined: (1) faster and more potent part of a computer, like multiple cores and broad sense GPU; (2) a computer program that utilizes these computational structures; and (3) essentially unlimited training data sets for a certain issue, like digital photos, digitalized files. The two stages of a computer-vision-based Machine Learning process are feature extraction and classification. Additional Machine Learning models, including ANN, CNN, RNN and others, can be applied for system training and optimization.

Название: Observability for Large Language Models: Understanding and Improving Your Use of LLMs
Автор: Phillip Carter
Издательство: O’Reilly Media, Inc.
Год: 2023-09-28
Язык: английский
Формат: pdf, mobi, epub
Размер: 10.2 MB

Artificial Intelligence (AI) has revolutionized numerous industries, enabling organizations to accomplish tasks and solve complex problems with unprecedented efficiency. In particular, large language models (LLMs) have emerged as powerful tools, demonstrating exceptional language-processing capabilities and fueling a surge in their adoption across a wide range of applications. From chatbots and language translation to content generation and data analysis, LLMs are being adopted by companies of all sizes and across all industries. As organizations eagerly embrace the potential of LLMs, the need to understand their behavior in production and use that understanding to improve development with them has become apparent. While the initial excitement surrounding LLMs often centers on accessing their remarkable capabilities with only a small up-front investment, it is crucial to acknowledge the significant problems that can arise after their initial implementation into a product. By introducing open-ended inputs in a product, organizations expose themselves to user behavior they’ve likely never seen before (and cannot possibly predict). LLMs are nondeterministic, meaning that the same inputs don’t always yield the same outputs, yet end users generally expect a degree of predictability in outputs. Organizations that lack good tools and data to understand systems in production may find themselves ill-prepared to tackle the challenges posed by a feature that uses LLMs