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Tag: Trendsr
An Introduction to Conditional Random Fields (Foundations and Trends(r) in Machine Learning)
Price:$85.00– $77.49
(as of Jan 22,2025 06:51:39 UTC – Details)
Publisher : Now Publishers (August 23, 2012)
Language : English
Paperback : 120 pages
ISBN-10 : 160198572X
ISBN-13 : 978-1601985729
Item Weight : 6.2 ounces
Dimensions : 6.14 x 0.25 x 9.21 inches
Conditional Random Fields (CRFs) are a popular and powerful framework for modeling structured prediction tasks in machine learning. In this post, we will provide an introduction to CRFs, focusing on the foundational concepts and recent advances in the field.CRFs are a type of probabilistic graphical model that captures dependencies between input variables and output variables. Unlike traditional models such as hidden Markov models or naive Bayes classifiers, CRFs allow for complex and flexible modeling of structured data, such as sequences, graphs, or images.
One of the key advantages of CRFs is their ability to model correlations between neighboring variables in the output space. This makes them particularly well-suited for tasks such as natural language processing, speech recognition, and computer vision, where the output is inherently structured and sequential.
In recent years, there has been significant progress in developing efficient algorithms for training and inference in CRFs, making them a practical choice for a wide range of machine learning applications. Additionally, researchers have explored extensions and generalizations of CRFs, such as deep CRFs and hierarchical CRFs, to further improve their performance and applicability.
Overall, CRFs are a versatile and powerful tool for modeling structured prediction tasks, and understanding their foundations and recent developments can be valuable for both researchers and practitioners in the field of machine learning. If you are interested in learning more about CRFs, we recommend checking out the book “Conditional Random Fields (Foundations and Trends® in Machine Learning)” for a comprehensive overview of the topic.
#Introduction #Conditional #Random #Fields #Foundations #Trendsr #Machine #Learning,machine learning: an applied mathematics introduction
Financial Machine Learning (Foundations and Trends(r) in Finance)
Financial Machine Learning (Foundations and Trends(r) in Finance)
Price : 59.21
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Financial Machine Learning (Foundations and Trends(r) in Finance)In today’s rapidly evolving financial landscape, the use of machine learning algorithms has become increasingly prevalent. From algorithmic trading to risk management, financial institutions are leveraging the power of machine learning to gain a competitive edge and drive more informed decision-making.
“Financial Machine Learning (Foundations and Trends(r) in Finance)” is a comprehensive book that explores the intersection of machine learning and finance. Authored by leading experts in the field, this book provides a deep dive into the foundations of machine learning techniques and how they can be applied to various financial applications.
Readers will learn about the latest methodologies and best practices for building predictive models, analyzing financial data, and optimizing trading strategies. Whether you are a seasoned finance professional or a data scientist looking to break into the financial industry, this book offers valuable insights and practical guidance for leveraging machine learning in finance.
Stay ahead of the curve and unlock the potential of financial machine learning with “Financial Machine Learning (Foundations and Trends(r) in Finance).”
#Financial #Machine #Learning #Foundations #Trendsr #Finance
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art (Foundations and Trends(r) in Computer Graphics and Vision)
Price:$99.00– $89.52
(as of Dec 27,2024 18:26:55 UTC – Details)
Publisher : Now Publishers (July 6, 2020)
Language : English
Paperback : 326 pages
ISBN-10 : 1680836889
ISBN-13 : 978-1680836882
Item Weight : 1.09 pounds
Dimensions : 6.14 x 0.69 x 9.21 inches
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art (Foundations and Trends(r) in Computer Graphics and Vision)In the rapidly evolving field of autonomous vehicles, computer vision plays a crucial role in enabling these vehicles to perceive and understand their surroundings. From detecting pedestrians and other vehicles to interpreting road signs and traffic signals, computer vision algorithms are essential for ensuring the safe and reliable operation of autonomous vehicles.
However, developing effective computer vision systems for autonomous vehicles is not without its challenges. From dealing with varying lighting conditions and weather to handling complex traffic scenarios and unexpected obstacles, there are numerous problems that need to be addressed to ensure the success of autonomous driving systems.
To tackle these challenges, researchers and engineers rely on a variety of datasets to train and test their computer vision algorithms. These datasets contain labeled images and videos that help algorithms learn to recognize objects and interpret their surroundings. Some popular datasets used in the field of autonomous vehicles include KITTI, Cityscapes, and ApolloScape.
In this post, we will explore the current state of the art in computer vision for autonomous vehicles, including recent advancements in object detection, semantic segmentation, and scene understanding. We will also discuss some of the key problems that researchers are working to solve, such as improving the robustness and reliability of computer vision systems in challenging real-world scenarios.
Overall, the field of computer vision for autonomous vehicles is rapidly advancing, with researchers making significant strides in developing more accurate and reliable algorithms. By staying up to date on the latest research and trends in this area, we can help drive the future of autonomous driving forward and ultimately make our roads safer for everyone.
#Computer #Vision #Autonomous #Vehicles #Problems #Datasets #State #Art #Foundations #Trendsr #Computer #Graphics #Vision
Beyond Fairness in Computer Vision: A Holistic Approach to Mitigating Harms and Fostering Community-Rooted Computer Vision Research (Foundations and Trends(r) in Computer Graphics and Vision)
Price: $80.00
(as of Dec 24,2024 10:00:49 UTC – Details)
Publisher : Now Publishers (September 30, 2024)
Language : English
Paperback : 118 pages
ISBN-10 : 1638283540
ISBN-13 : 978-1638283546
Item Weight : 6.2 ounces
Dimensions : 6.14 x 0.25 x 9.21 inches
In recent years, computer vision has made significant advancements in various applications, from healthcare to autonomous vehicles. However, there is growing concern about the fairness and ethical implications of computer vision systems, particularly in areas such as bias, privacy, and discrimination.In our latest research paper, “Beyond Fairness in Computer Vision: A Holistic Approach to Mitigating Harms and Fostering Community-Rooted Computer Vision Research,” published in Foundations and Trends(r) in Computer Graphics and Vision, we propose a comprehensive framework for addressing these challenges.
Our approach goes beyond traditional fairness metrics and focuses on creating community-rooted computer vision research that is inclusive, transparent, and accountable. We emphasize the importance of involving diverse stakeholders, including marginalized communities, in the design and evaluation of computer vision systems to ensure that they are fair and equitable.
Furthermore, we highlight the need for interdisciplinary collaboration between computer vision researchers, ethicists, policymakers, and community advocates to address the complex ethical issues surrounding computer vision technology. By taking a holistic approach, we can mitigate harms and foster a more responsible and socially conscious use of computer vision systems.
We believe that our framework provides a roadmap for advancing the field of computer vision in a way that benefits society as a whole. We invite researchers, practitioners, and policymakers to join us in this important conversation and work together to build a more ethical and inclusive future for computer vision technology.
#Fairness #Computer #Vision #Holistic #Approach #Mitigating #Harms #Fostering #CommunityRooted #Computer #Vision #Research #Foundations #Trendsr #Computer #Graphics #Vision
Advances and Open Problems in Federated Learning (Foundations and Trends(r) in Machine Learning)
Price:$99.00– $59.79
(as of Dec 24,2024 05:15:01 UTC – Details)
Advances and Open Problems in Federated Learning (Foundations and Trends® in Machine Learning)Federated learning has emerged as a promising approach for training machine learning models on distributed data sources while ensuring data privacy and security. In recent years, significant advances have been made in the field, but there are still many open problems that need to be addressed.
One of the key advances in federated learning is the development of more efficient and scalable algorithms. Researchers have proposed novel optimization techniques, such as federated averaging and federated optimization, to reduce communication overhead and improve convergence speed. These advancements have enabled federated learning to be applied to a wide range of applications, including healthcare, finance, and Internet of Things (IoT) devices.
Another important development in federated learning is the integration of differential privacy mechanisms to protect sensitive data during the training process. By adding noise to the gradients or model updates, differential privacy ensures that individual data points cannot be inferred from the trained model. This has paved the way for federated learning to be used in highly regulated industries where data privacy is a top priority.
Despite these advancements, there are still several open problems in federated learning that need to be addressed. One of the main challenges is the heterogeneity of data and computing resources across different devices. Designing algorithms that can handle this heterogeneity while ensuring model performance and convergence is a major research direction in the field.
Another open problem is the robustness of federated learning against adversarial attacks. Since the training process is distributed across multiple devices, attackers can potentially manipulate the training data or model updates to compromise the integrity of the trained model. Developing defense mechanisms against such attacks is a critical area of research in federated learning.
In conclusion, federated learning has made significant strides in recent years, but there are still many challenges that need to be overcome. By addressing these open problems, researchers can continue to advance the field and unlock the full potential of federated learning in real-world applications.
#Advances #Open #Problems #Federated #Learning #Foundations #Trendsr #Machine #Learning
Deep Learning: Methods and Applications (Foundations and Trends(r) i – VERY GOOD
Deep Learning: Methods and Applications (Foundations and Trends(r) i – VERY GOOD
Price : 50.27
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Deep Learning: Methods and Applications (Foundations and Trends®) – A Comprehensive GuideDeep learning has revolutionized the field of artificial intelligence and has led to significant advancements in a wide range of applications. In this post, we will explore the methods and applications of deep learning, as discussed in the book “Deep Learning: Methods and Applications” by the leading experts in the field.
The book provides a comprehensive overview of the foundations of deep learning, including neural networks, backpropagation, and optimization techniques. It also covers advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
One of the key strengths of the book is its focus on real-world applications of deep learning. It provides case studies and examples of how deep learning is being used in areas such as computer vision, natural language processing, and reinforcement learning. The authors also discuss the challenges and limitations of deep learning, as well as potential future directions for research.
Overall, “Deep Learning: Methods and Applications” is a highly recommended resource for anyone interested in understanding the latest developments in deep learning. Whether you are a student, researcher, or practitioner in the field, this book will provide valuable insights and practical guidance for applying deep learning techniques to solve complex problems.
#Deep #Learning #Methods #Applications #Foundations #Trendsr #GOOD
Deep Learning for Matching in Search and Recommendation (Foundations and Trends(r) in Information Retrieval)
Price:$99.00– $90.81
(as of Dec 18,2024 02:21:16 UTC – Details)
Publisher : Now Publishers (July 14, 2020)
Language : English
Paperback : 200 pages
ISBN-10 : 1680837060
ISBN-13 : 978-1680837063
Item Weight : 11.2 ounces
Dimensions : 6.14 x 0.43 x 9.21 inches
Deep Learning for Matching in Search and RecommendationIn the world of information retrieval, the ability to accurately match user queries with relevant content is crucial for providing a seamless and personalized user experience. Traditional methods of matching have often relied on hand-crafted features and rules, but deep learning approaches have shown great promise in improving the accuracy and efficiency of matching algorithms.
“Deep Learning for Matching in Search and Recommendation” is a comprehensive guide that explores the foundations and trends of using deep learning techniques for improving matching in search and recommendation systems. This book delves into the various deep learning models and architectures that have been successfully applied to matching tasks, such as neural networks, convolutional neural networks, and recurrent neural networks.
The authors provide a detailed overview of the theoretical underpinnings of deep learning for matching, as well as practical guidance on how to implement and optimize deep learning models for search and recommendation applications. They also discuss the challenges and limitations of using deep learning for matching, and offer insights into future research directions in this exciting field.
Whether you are a researcher, practitioner, or student interested in the intersection of deep learning and information retrieval, “Deep Learning for Matching in Search and Recommendation” is a must-read resource that will deepen your understanding of the latest advancements in this rapidly evolving field.
#Deep #Learning #Matching #Search #Recommendation #Foundations #Trendsr #Information #Retrieval
Model-Based Deep Learning (Foundations and Trends(r) in Signal Processing)
Model-Based Deep Learning (Foundations and Trends(r) in Signal Processing)
Price : 95.28
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Model-Based Deep Learning: Foundations and Trends in Signal ProcessingIn the rapidly evolving field of deep learning, the integration of models plays a crucial role in improving the efficiency and effectiveness of algorithms. Model-based deep learning has emerged as a promising approach to harness the power of neural networks while incorporating domain knowledge and constraints.
The book “Model-Based Deep Learning” delves into the foundations and trends of this exciting area of research, providing readers with a comprehensive overview of the key principles and techniques involved. From the basics of neural networks to advanced model-based optimization methods, this book covers a wide range of topics essential for understanding and implementing model-based deep learning algorithms.
Whether you are a seasoned researcher looking to expand your knowledge or a newcomer hoping to gain a deeper understanding of deep learning, “Model-Based Deep Learning” is a valuable resource that will guide you through the intricacies of this cutting-edge technology. Stay ahead of the curve and take your deep learning skills to the next level with the insights and strategies offered in this groundbreaking book.
#ModelBased #Deep #Learning #Foundations #Trendsr #Signal #Processing
Causal Deep Learning: Encouraging Impact on Real-World Problems Through Causality (Foundations and Trends(r) in Signal Processing)
Price: $85.00
(as of Dec 16,2024 12:57:33 UTC – Details)
Publisher : Now Publishers (August 1, 2024)
Language : English
Paperback : 124 pages
ISBN-10 : 1638284008
ISBN-13 : 978-1638284000
Item Weight : 6.6 ounces
Dimensions : 6.14 x 0.27 x 9.21 inches
Causal Deep Learning: Encouraging Impact on Real-World Problems Through Causality (Foundations and Trends(r) in Signal Processing)In recent years, deep learning has revolutionized many fields, from computer vision to natural language processing. However, despite its impressive performance on a wide range of tasks, deep learning models often lack interpretability and generalizability. This has led researchers to explore the integration of causal reasoning into deep learning frameworks, in order to not only make predictions but also understand the underlying mechanisms and relationships between variables.
In a recent publication in Foundations and Trends(r) in Signal Processing, researchers delve into the emerging field of causal deep learning and its potential to address real-world problems more effectively. By incorporating causal reasoning into deep learning models, researchers aim to not only predict outcomes but also understand the causal relationships between variables and make interventions to bring about desired outcomes.
The authors highlight the importance of causality in deep learning, as it allows for more robust and reliable models that can generalize to new data and provide explanations for their predictions. By understanding the causal mechanisms at play, researchers can uncover hidden patterns, identify confounding variables, and make more informed decisions in various domains such as healthcare, finance, and autonomous systems.
Overall, the integration of causality into deep learning has the potential to revolutionize the way we approach complex problems and make more impactful contributions to society. As researchers continue to explore the intersection of causality and deep learning, we can expect to see even greater advancements in the field and more meaningful applications in the real world.
#Causal #Deep #Learning #Encouraging #Impact #RealWorld #Problems #Causality #Foundations #Trendsr #Signal #Processing
Datacenter Power Management in Smart Grids (Foundations and Trends(r) in Electronic Design Automation)
Price: $80.34
(as of Dec 16,2024 03:44:47 UTC – Details)
Publisher : Now Publishers (January 19, 2015)
Language : English
Paperback : 114 pages
ISBN-10 : 1601987927
ISBN-13 : 978-1601987921
Item Weight : 6.1 ounces
Dimensions : 6.14 x 0.24 x 9.21 inches
Datacenter Power Management in Smart GridsIn the world of electronic design automation, one of the key challenges faced by data centers is efficiently managing power consumption. With the increasing demand for cloud computing services and the proliferation of Internet of Things devices, data centers are consuming more and more electricity, leading to higher costs and environmental concerns.
Smart grids offer a promising solution to this problem by enabling data centers to dynamically adjust their power consumption based on real-time electricity prices and grid conditions. By leveraging advanced algorithms and machine learning techniques, data centers can optimize their energy usage, reduce costs, and contribute to a more sustainable energy infrastructure.
The recent research in this field, as presented in the book “Datacenter Power Management in Smart Grids” by Foundations and Trends(r) in Electronic Design Automation, provides a comprehensive overview of the latest developments and best practices in data center power management. From demand response strategies to predictive analytics and energy storage solutions, this book offers valuable insights for data center operators, engineers, and researchers looking to enhance their energy efficiency and sustainability efforts.
By embracing smart grid technologies and adopting innovative power management strategies, data centers can not only reduce their environmental footprint but also improve their bottom line. With the right tools and knowledge, data center operators can pave the way for a more efficient and sustainable future in electronic design automation.
#Datacenter #Power #Management #Smart #Grids #Foundations #Trendsr #Electronic #Design #Automation
