Advanced Machine Learning Algorithms
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Advanced Machine Learning Algorithms
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Author : Mr. Digantkumar Parmar
language : en
Publisher: Xoffencer International Book Publication House
Release Date : 2025-05-22
Advanced Machine Learning Algorithms written by Mr. Digantkumar Parmar and has been published by Xoffencer International Book Publication House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-22 with Computers categories.
By enabling systems to do complicated tasks such as image recognition, natural language comprehension, autonomous decision making, and predictive analytics with surprising precision and flexibility, advanced machine learning algorithms represent the leading edge of artificial intelligence. These algorithms empower computers to accomplish these tasks. Deep learning, ensemble learning, reinforcement learning, Bayesian approaches, and evolutionary algorithms are some of the advanced paradigms that are included into these algorithms. To manage large-scale, high-dimensional, and frequently noisy data, these algorithms go beyond the limits of classic statistical methods. A wide variety of fields, including computer vision and voice processing, drug discovery, and financial modelling, have been revolutionized as a result of the creation of designs such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and Graph Neural Networks (GNNs). The limitations of what computers are capable of learning with minimum supervision and data are being pushed further by meta-learning, self-supervised learning, and generative models such as GANs and VAEs. Interpretability, data efficiency, robustness, and ethical deployment continue to be challenging areas, notwithstanding the progress that has been made. The fundamental ideas, applications, and ongoing research trends in advanced machine learning algorithms are discussed in this book. The book also highlights the transformational potential of these algorithms as well as the crucial concerns that need to be addressed in order to guarantee that they are used in real-world systems in a responsible, equitable, and safe manner.
Advanced Machine Learning And Deep Learning Algorithms
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Author : Dr.R.Balamanigandan
language : en
Publisher: SK Research Group of Companies
Release Date : 2024-12-21
Advanced Machine Learning And Deep Learning Algorithms written by Dr.R.Balamanigandan and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-21 with Computers categories.
Dr.R.Balamanigandan, Professor & Head, Department of Neural Networks, Institute of Computer Science & Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India. Dr.V.P.Gladis Pushparathi, Professor & Head, Department of CSE, Velammal Institute of Technology, Panchatti, Thiruvallur, Tamil Nadu, India. Mr.Sai Srinivas Vellela, Assistant Professor, Department of Computer Science & Engineering - Data Science, Chalapathi Institute of Technology, Guntur, Andhra Pradesh, India. Mrs.A.Mary Jenifer, JRF, Department of Neural Networks, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
Mastering Machine Learning On Aws
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Author : Dr. Saket S.R. Mengle
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-20
Mastering Machine Learning On Aws written by Dr. Saket S.R. Mengle and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-20 with Computers categories.
Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key FeaturesBuild machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark and TensorFlowLearn model optimization, and understand how to scale your models using simple and secure APIsDevelop, train, tune and deploy neural network models to accelerate model performance in the cloudBook Description AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. What you will learnManage AI workflows by using AWS cloud to deploy services that feed smart data productsUse SageMaker services to create recommendation modelsScale model training and deployment using Apache Spark on EMRUnderstand how to cluster big data through EMR and seamlessly integrate it with SageMakerBuild deep learning models on AWS using TensorFlow and deploy them as servicesEnhance your apps by combining Apache Spark and Amazon SageMakerWho this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.
Advanced Machine Learning Algorithms
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Author : Mr. Rajesh Sen
language : en
Publisher: Xoffencerpublication
Release Date : 2024-04-18
Advanced Machine Learning Algorithms written by Mr. Rajesh Sen and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-18 with Computers categories.
The field of artificial intelligence has reached a greater degree of complexity with the introduction of advanced machine learning algorithms. When compared to more conventional approaches, these algorithms are more exhaustive in their examination of data analysis, pattern detection, and decision-making procedures. This is an overview that serves as an introduction. Deep learning is a subfield of machine learning in which artificial neural networks, which are modelled after the structure and function of the human brain, are taught to discover new information by analyzing huge volumes of data. For example, Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) for sequential data analysis are examples of deep learning models that have achieved great success in a variety of disciplines, including computer vision, natural language processing, and speech recognition. Through the process of reinforcement learning, agents are taught to make sequences of decisions within an environment in order to maximize the accumulation of overall rewards. Reinforcement learning agents learn by trial and error, getting feedback in the form of incentives or penalties. This is in contrast to supervised learning, which offers the model data that has been labelled. The use of this strategy has shown to be effective in a variety of domains, including robotics, autonomous vehicle control, and game playing (for example, AlphaGo). Deep learning models that fall into the GAN category were first presented by Ian Good fellow in the year 2014. Generalized adversarial networks (GANs) are made up of two neural networks—a generator and a discriminator—that are trained concurrently in a competitive environment. It is the discriminator's job to learn how to distinguish between genuine and false data, while the generator is responsible for learning how to make synthetic data samples that are similar to actual data. Application areas for GANs include the production of images, the enhancement of data, and the transfer of styles. This particular sort of deep learning model, known as transformers, has been increasingly popular in the field of natural language processing (NLP) initiatives. Transformers, in contrast to more conventional sequence models such as recurrent neural networks (RNNs) and long short-term
The International Conference On Advanced Machine Learning Technologies And Applications Amlta2018
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Author : Aboul Ella Hassanien
language : en
Publisher: Springer
Release Date : 2018-01-25
The International Conference On Advanced Machine Learning Technologies And Applications Amlta2018 written by Aboul Ella Hassanien and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-25 with Technology & Engineering categories.
This book presents the refereed proceedings of the third International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2018, held in Cairo, Egypt, on February 22–24, 2018, and organized by the Scientific Research Group in Egypt (SRGE). The papers cover current research in machine learning, big data, Internet of Things, biomedical engineering, fuzzy logic, security, and intelligence swarms and optimization.
Advanced Machine Learning Ai And Cybersecurity In Web3 Theoretical Knowledge And Practical Application
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Author : Bouarara, Hadj Ahmed
language : en
Publisher: IGI Global
Release Date : 2024-08-23
Advanced Machine Learning Ai And Cybersecurity In Web3 Theoretical Knowledge And Practical Application written by Bouarara, Hadj Ahmed and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-23 with Computers categories.
In the evolving landscape of Web3, the use of advanced machine learning, artificial intelligence, and cybersecurity transforms industries through theoretical exploration and practical application. The integration of advanced machine learning and AI techniques promises enhanced security protocols, predictive analytics, and adaptive defenses against the increasing number of cyber threats. However, these technological improvements also raise questions regarding privacy, transparency, and the ethical implications of AI-driven security measures. Advanced Machine Learning, AI, and Cybersecurity in Web3: Theoretical Knowledge and Practical Application explores theories and applications of improved technological techniques in Web 3.0. It addresses the challenges inherent to decentralization while harnessing the benefits offered by advances, thereby paving the way for a safer and more advanced digital era. Covering topics such as fraud detection, cryptocurrency, and data management, this book is a useful resource for computer engineers, financial institutions, security and IT professionals, business owners, researchers, scientists, and academicians.
Practical And Advanced Machine Learning Methods For Model Risk Management
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Author : INDRA REDDY MALLELA NAGARJUNA PUTTA PROF.(DR.) AVNEESH KUMAR
language : en
Publisher: DeepMisti Publication
Release Date : 2024-12-22
Practical And Advanced Machine Learning Methods For Model Risk Management written by INDRA REDDY MALLELA NAGARJUNA PUTTA PROF.(DR.) AVNEESH KUMAR and has been published by DeepMisti Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-22 with Computers categories.
In today’s fast-evolving landscape of artificial intelligence (AI) and machine learning (ML), organizations are increasingly relying on advanced models to drive decision-making and innovation across various sectors. As machine learning technologies grow in complexity and scale, managing the risks associated with these models becomes a critical concern. From biases in algorithms to the interpretability of predictions, the potential for errors and unintended consequences demands rigorous frameworks for assessing and mitigating risks. "Practical and Advanced Machine Learning Methods for Model Risk Management" explores these challenges in depth. It is designed to provide both foundational knowledge and advanced techniques for effectively managing model risks throughout their lifecycle—from development and deployment to monitoring and updating. This book caters to professionals working in data science, machine learning engineering, risk management, and governance, offering a comprehensive understanding of how to balance model performance with robust risk management practices. The book begins with a strong foundation in the principles of model risk management (MRM), exploring the core concepts of risk identification, assessment, and mitigation. From there, it dives into more advanced techniques for managing risks in complex ML models, including methods for ensuring model fairness, transparency, and interpretability, as well as strategies for dealing with adversarial attacks, data security concerns, and ethical considerations. Throughout, we emphasize the importance of collaboration between data scientists, risk professionals, and organizational leaders in creating a culture of responsible AI. This collaborative approach is crucial for building models that not only perform at the highest levels but also adhere to ethical standards and regulatory requirements. By the end of this book, readers will have a deep understanding of the critical role that risk management plays in AI and machine learning, as well as the practical tools and methods needed to implement a comprehensive MRM strategy. Whether you are just beginning your journey in model risk management or are seeking to refine your existing processes, this book serves as an essential resource for navigating the complexities of machine learning in today’s rapidly changing technological landscape. We hope this book equips you with the knowledge to effectively address the risks of ML models and apply these insights to improve both the performance and trustworthiness of your AI systems. Thank you for embarking on this journey with us. Authors
Machine Learning Foundations
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Author : Taeho Jo
language : en
Publisher: Springer Nature
Release Date : 2021-02-12
Machine Learning Foundations written by Taeho Jo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-12 with Technology & Engineering categories.
This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.
Next Generation Wireless Networks Meet Advanced Machine Learning Applications
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Author : Comşa, Ioan-Sorin
language : en
Publisher: IGI Global
Release Date : 2019-01-25
Next Generation Wireless Networks Meet Advanced Machine Learning Applications written by Comşa, Ioan-Sorin and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-25 with Technology & Engineering categories.
The ever-evolving wireless technology industry is demanding new technologies and standards to ensure a higher quality of experience for global end-users. This developing challenge has enabled researchers to identify the present trend of machine learning as a possible solution, but will it meet business velocity demand? Next-Generation Wireless Networks Meet Advanced Machine Learning Applications is a pivotal reference source that provides emerging trends and insights into various technologies of next-generation wireless networks to enable the dynamic optimization of system configuration and applications within the fields of wireless networks, broadband networks, and wireless communication. Featuring coverage on a broad range of topics such as machine learning, hybrid network environments, wireless communications, and the internet of things; this publication is ideally designed for industry experts, researchers, students, academicians, and practitioners seeking current research on various technologies of next-generation wireless networks.
Ai Mastery Advanced Artificial Intelligence Concepts Book 3
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Author : Dizzy Davidson
language : en
Publisher: Pure Water Books
Release Date : 2024-09-11
Ai Mastery Advanced Artificial Intelligence Concepts Book 3 written by Dizzy Davidson and has been published by Pure Water Books this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-11 with Computers categories.
Are you struggling to fully understand AI and automation? You’re not alone. Many grapple with the complexities of advanced AI concepts and their practical applications. But what if you could master these topics with ease? “AI Mastery: Advanced Artificial Intelligence Concepts, Book 3” is your definitive guide to conquering advanced AI. This book demystifies complex algorithms, reinforcement learning, AI in robotics, and big data analytics, providing you with the knowledge and tools to excel. Benefits of reading this book: Deep Dive into Advanced Algorithms: Understand and implement sophisticated machine learning algorithms. Master Reinforcement Learning: Learn key concepts and see real-world applications. Integrate AI with Robotics: Explore how AI enhances robotic systems through detailed case studies. Harness Big Data: Discover the role of AI in big data analytics and the tools to leverage it. This book is an essential resource for anyone looking to advance their AI knowledge. Whether you’re a student, professional, or enthusiast, “AI Mastery” offers hands-on projects and bonus content to solidify your expertise. Why this book? Comprehensive Coverage: From advanced algorithms to big data, this book covers all critical areas. Practical Insights: Real-world examples and case studies make complex concepts accessible. Expert Guidance: Learn from detailed explanations and expert insights. Get this book now to unlock the full potential of AI and automation. Transform your understanding and become an AI expert today! Viral Bullet Points Detailed study of advanced machine learning algorithms Comprehensive guide to reinforcement learning Integration of AI and robotics with real-world case studies Role of AI in big data analytics Hands-on advanced projects for practical experience Call to Action: Don’t miss out on mastering advanced AI concepts. Get your copy of “AI Mastery: Advanced Artificial Intelligence Concepts, Book 3” today and take your AI knowledge to the next level!