Download or read book AI Modelling and Process written by PRADEEP K. SURI and published by Blue Rose Publishers. This book was released on 2024-01-08 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book delves into the fascinating world of Deep Neural Networks (DNNs), a powerful tool within Artificial Intelligence (AI). It explores how DNNs learn and make predictions based on data. Clarifying DNNs: The book explains the core concepts of DNNs, their structure, and how they extract patterns from training data. Data preparation: Understanding the importance of various datasets, including training, testing, and unseen data, in building robust AI models. Machine Learning and Deep Learning: The book provides a clear overview of Machine Learning (ML) and Deep Learning (DL) as foundational concepts for AI development. Python libraries: Learn about Python libraries commonly used for AI and DNN implementation. Designing AI Management Dashboards: Discover how to create dashboards to visualize and monitor AI performance. Real-world applications of AI: Explore the diverse domains where AI is making a significant impact, including finance, and healthcare. AI Engine integration: Understand the benefits of integrating AI engines with existing systems like ERP. Generative AI: Learn about this exciting subfield of AI focused on creating new data. 50+ video tutorials: The book's website offers video tutorials demonstrating AI forecasting in various domains. Overall, "AI-Modelling and Process" provides a valuable roadmap for understanding and implementing AI in today's data-driven world.
Download or read book Research Directions in Computational Mechanics written by National Research Council and published by National Academies Press. This book was released on 1991-02-01 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational mechanics is a scientific discipline that marries physics, computers, and mathematics to emulate natural physical phenomena. It is a technology that allows scientists to study and predict the performance of various productsâ€"important for research and development in the industrialized world. This book describes current trends and future research directions in computational mechanics in areas where gaps exist in current knowledge and where major advances are crucial to continued technological developments in the United States.
Download or read book Practical Natural Language Processing written by Sowmya Vajjala and published by O'Reilly Media. This book was released on 2020-06-17 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective
Download or read book AI Driven Intelligent Models for Business Excellence written by Samala Nagaraj and published by IGI Global. This book was released on 2022 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: "As digital technology is taking the world in a revolutionary way and business related aspects are getting smarter this book is a potential research source on the Artificial Intelligence-based Business Applications and Intelligence"--
Download or read book Explanatory Model Analysis written by Przemyslaw Biecek and published by CRC Press. This book was released on 2021-02-15 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.
Download or read book Next Generation AI Language Models in Research written by Kashif Naseer Qureshi and published by CRC Press. This book was released on 2024-11-13 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this comprehensive and cutting-edge volume, Qureshi and Jeon bring together experts from around the world to explore the potential of artificial intelligence models in research and discuss the potential benefits and the concerns and challenges that the rapid development of this field has raised. The international chapter contributor group provides a wealth of technical information on different aspects of AI, including key aspects of AI, deep learning and machine learning models for AI, natural language processing and computer vision, reinforcement learning, ethics and responsibilities, security, practical implementation, and future directions. The contents are balanced in terms of theory, methodologies, and technical aspects, and contributors provide case studies to clearly illustrate the concepts and technical discussions throughout. Readers will gain valuable insights into how AI can revolutionize their work in fields including data analytics and pattern identification, healthcare research, social science research, and more, and improve their technical skills, problem-solving abilities, and evidence-based decision-making. Additionally, they will be cognizant of the limitations and challenges, the ethical implications, and security concerns related to language models, which will enable them to make more informed choices regarding their implementation. This book is an invaluable resource for undergraduate and graduate students who want to understand AI models, recent trends in the area, and technical and ethical aspects of AI. Companies involved in AI development or implementing AI in various fields will also benefit from the book’s discussions on both the technical and ethical aspects of this rapidly growing field.
Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Download or read book Artificial Intelligence in Asset Management written by Söhnke M. Bartram and published by CFA Institute Research Foundation. This book was released on 2020-08-28 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.
Download or read book Smart Process Engineering Using Artificial Intelligence Tools to Learn from Graph based Chemical Plant and Process Data written by Jonas Eric Oeing and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: While Artificial Intelligence (AI) methods are already present in many consumer applications, they are currently less used and accepted in the process industry and in the field of engineering. At the same time, the availability of digital engineering data is increasing due to the increasing digitization and standardization of data formats, for example DEXPI (Data Exchange in Process Industry).This work provides answers concerning important questions that need to be addressed when applying AI methods in engineering. How must plant data be structured and which standards can already be accessed? Which areas of engineering are promising for the application of AI methods and which models can be used?In this context, new and innovative approaches for data-driven modeling of engineering data will be introduced, applied and evaluated. A graph-based, machine-learning information model is used to provide a basis for describing information from plant topologies in the form of a piping and instrumentation diagram (P&ID) and process simulations based on unit operations. The use of machine learning models enables to learn the relationship between process data and separation units in conceptual engineering to support the generation of downstreamprocesses. In detail engineering, AI algorithms are used that learn the topology of process units. In this way, Recurrent Neural Networks predict P&ID components, while Graph Neural Networks check the consistency to support the engineering and drawing of P&IDs. Thecombination of the graph-based information model with a deterministic algorithm also enables automated safety assessments in an early engineering and design phase.The different modeling approaches will be validated in initial feasibility studies and integrated into a smart engineering workflow and first prototype applications as part of this work.
Download or read book Automating Business Modelling written by Yun-Heh Chen-Burger and published by Springer Science & Business Media. This book was released on 2005-11-27 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Enhances the use of enterprise models as an effective communication medium between business and technical personnel. Details the blue-print of the to-be developed business system.
Download or read book Hybrid Modeling and Optimization of Manufacturing written by Ramón Quiza and published by Springer Science & Business Media. This book was released on 2012-02-15 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) techniques and the finite element method (FEM) are both powerful computing tools, which are extensively used for modeling and optimizing manufacturing processes. The combination of these tools has resulted in a new flexible and robust approach as several recent studies have shown. This book aims to review the work already done in this field as well as to expose the new possibilities and foreseen trends. The book is expected to be useful for postgraduate students and researchers, working in the area of modeling and optimization of manufacturing processes.
Download or read book Artificial Intelligence in Process Engineering written by Michael Mavrovouniotis and published by Elsevier. This book was released on 2012-12-02 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence in Process Engineering aims to present a diverse sample of Artificial Intelligence (AI) applications in process engineering. The book contains contributions, selected by the editors based on educational value and diversity of AI methods and process engineering application domains. Topics discussed in the text include the use of qualitative reasoning for modeling and simulation of chemical systems; the use of qualitative models in discrete event simulation to analyze malfunctions in processing systems; and the diagnosis of faults in processes that are controlled by Programmable Logic Controllers. There are also debates on the issue of quantitative versus qualitative information. The control of batch processes, a design of a system that synthesizes bioseparation processes, and process design in the domain of chemical (rather than biochemical) systems are likewise covered in the text. This publication will be of value to industrial engineers and process engineers and researchers.
Download or read book Artificial Intelligence for Business written by Jason L. Anderson and published by John Wiley & Sons. This book was released on 2020-04-21 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence for Business: A Roadmap for Getting Started with AI will provide the reader with an easy to understand roadmap for how to take an organization through the adoption of AI technology. It will first help with the identification of which business problems and opportunities are right for AI and how to prioritize them to maximize the likelihood of success. Specific methodologies are introduced to help with finding critical training data within an organization and how to fill data gaps if they exist. With data in hand, a scoped prototype can be built to limit risk and provide tangible value to the organization as a whole to justify further investment. Finally, a production level AI system can be developed with best practices to ensure quality with not only the application code, but also the AI models. Finally, with this particular AI adoption journey at an end, the authors will show that there is additional value to be gained by iterating on this AI adoption lifecycle and improving other parts of the organization.
Download or read book Generative AI and LLMs written by S. Balasubramaniam and published by Walter de Gruyter GmbH & Co KG. This book was released on 2024-09-23 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms that operate in an unsupervised or semi-supervised manner. These algorithms leverage pre-existing content, such as text, photos, audio, video, and code, to generate novel content. The primary objective is to produce authentic and novel material. In addition, there exists an absence of constraints on the quantity of novel material that they are capable of generating. New material can be generated through the utilization of Application Programming Interfaces (APIs) or natural language interfaces, such as the ChatGPT developed by Open AI and Bard developed by Google. The field of generative artificial intelligence (AI) stands out due to its unique characteristic of undergoing development and maturation in a highly transparent manner, with its progress being observed by the public at large. The current era of artificial intelligence is being influenced by the imperative to effectively utilise its capabilities in order to enhance corporate operations. Specifically, the use of large language model (LLM) capabilities, which fall under the category of Generative AI, holds the potential to redefine the limits of innovation and productivity. However, as firms strive to include new technologies, there is a potential for compromising data privacy, long-term competitiveness, and environmental sustainability. This book delves into the exploration of generative artificial intelligence (GAI) and LLM. It examines the historical and evolutionary development of generative AI models, as well as the challenges and issues that have emerged from these models and LLM. This book also discusses the necessity of generative AI-based systems and explores the various training methods that have been developed for generative AI models, including LLM pretraining, LLM fine-tuning, and reinforcement learning from human feedback. Additionally, it explores the potential use cases, applications, and ethical considerations associated with these models. This book concludes by discussing future directions in generative AI and presenting various case studies that highlight the applications of generative AI and LLM.
Download or read book Explainable Interpretable and Transparent AI Systems written by B. K. Tripathy and published by CRC Press. This book was released on 2024-08-23 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transparent Artificial Intelligence (AI) systems facilitate understanding of the decision-making process and provide opportunities in various aspects of explaining AI models. This book provides up-to-date information on the latest advancements in the field of explainable AI, which is a critical requirement of AI, Machine Learning (ML), and Deep Learning (DL) models. It provides examples, case studies, latest techniques, and applications from domains such as healthcare, finance, and network security. It also covers open-source interpretable tool kits so that practitioners can use them in their domains. Features: Presents a clear focus on the application of explainable AI systems while tackling important issues of “interpretability” and “transparency”. Reviews adept handling with respect to existing software and evaluation issues of interpretability. Provides insights into simple interpretable models such as decision trees, decision rules, and linear regression. Focuses on interpreting black box models like feature importance and accumulated local effects. Discusses capabilities of explainability and interpretability. This book is aimed at graduate students and professionals in computer engineering and networking communications.
Download or read book MACHINE LEARNING INTERPRETABILITY EXPLAINING AI MODELS TO HUMANS written by Dr. Faisal Alghayadh and published by Xoffencerpublication. This book was released on 2024-01-10 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: Within the ever-evolving realm of artificial intelligence (AI), the field of Machine Learning Interpretability (MLI) has surfaced as a crucial conduit, serving as a vital link between the intricate nature of sophisticated AI models and the pressing necessity for lucid decision-making procedures in practical scenarios. With the progressive integration of AI systems across various domains, ranging from healthcare to finance, there arises an escalating need for transparency and accountability concerning the operational mechanisms of these intricate models. The pursuit of interpretability in machine learning is of paramount importance in comprehending the enigmatic essence of artificial intelligence. It provides a structured methodology to unravel the intricate mechanisms of algorithms, thereby rendering their outputs intelligible to human stakeholders. The Multimodal Linguistic Interface (MLI) functions as a pivotal conduit, bridging the dichotomous domains of binary machine intelligence and the intricate cognitive faculties of human comprehension. Its primary purpose lies in fostering a mutually beneficial association, wherein the potential of artificial intelligence can be harnessed with efficacy and conscientiousness. The transition from perceiving AI as a "black box" to embracing a more transparent and interpretable framework represents a significant paradigm shift. This shift not only fosters trust in AI technologies but also empowers various stakeholders such as end-users, domain experts, and policymakers. By gaining a deeper understanding of AI model outputs, these stakeholders are equipped to make informed decisions with confidence. In the current epoch characterized by remarkable progress in technology, the importance of Machine Learning Interpretability is underscored as a pivotal element for the conscientious and ethical implementation of AI. This development heralds a novel era wherein artificial intelligence harmoniously interfaces with human intuition and expertise
Download or read book Enterprise Artificial Intelligence Transformation written by Rashed Haq and published by John Wiley & Sons. This book was released on 2020-06-10 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: Enterprise Artificial Intelligence Transformation AI is everywhere. From doctor's offices to cars and even refrigerators, AI technology is quickly infiltrating our daily lives. AI has the ability to transform simple tasks into technological feats at a human level. This will change the world, plain and simple. That's why AI mastery is such a sought-after skill for tech professionals. Author Rashed Haq is a subject matter expert on AI, having developed AI and data science strategies, platforms, and applications for Publicis Sapient's clients for over 10 years. He shares that expertise in the new book, Enterprise Artificial Intelligence Transformation. The first of its kind, this book grants technology leaders the insight to create and scale their AI capabilities and bring their companies into the new generation of technology. As AI continues to grow into a necessary feature for many businesses, more and more leaders are interested in harnessing the technology within their own organizations. In this new book, leaders will learn to master AI fundamentals, grow their career opportunities, and gain confidence in machine learning. Enterprise Artificial Intelligence Transformation covers a wide range of topics, including: Real-world AI use cases and examples Machine learning, deep learning, and slimantic modeling Risk management of AI models AI strategies for development and expansion AI Center of Excellence creating and management If you're an industry, business, or technology professional that wants to attain the skills needed to grow your machine learning capabilities and effectively scale the work you're already doing, you'll find what you need in Enterprise Artificial Intelligence Transformation.
