Zion Tech Group

Tag: Lectures

Optimization Algorithms for Distributed Machine Learning (Synthesis Lectures on Learning, Networks, and Algorithms)

Price: $44.99
(as of Dec 24,2024 23:07:22 UTC – Details)

Publisher ‏ : ‎ Springer; 1st ed. 2023 edition (November 26, 2022)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 140 pages
ISBN-10 ‏ : ‎ 3031190661
ISBN-13 ‏ : ‎ 978-3031190667
Item Weight ‏ : ‎ 15.2 ounces
Dimensions ‏ : ‎ 6.38 x 0.39 x 9.29 inches

Optimization Algorithms for Distributed Machine Learning (Synthesis Lectures on Learning, Networks, and Algorithms)

Distributed machine learning has become increasingly popular due to the growing volume of data and the need to process it efficiently. In this post, we will explore the different optimization algorithms that are used in distributed machine learning, as discussed in the book “Optimization Algorithms for Distributed Machine Learning” by Francesco Orabona and Sashank J. Reddi.

The book provides a comprehensive overview of various optimization algorithms that are specifically designed for distributed machine learning. It covers a range of topics, including optimization theory, distributed computing, and machine learning algorithms.

Some of the key topics covered in the book include:

– Distributed optimization algorithms such as distributed gradient descent, distributed proximal methods, and distributed primal-dual methods.
– Communication-efficient algorithms for distributed optimization, which aim to minimize the amount of communication between nodes in a distributed system.
– Parallel and distributed algorithms for large-scale machine learning problems, such as deep learning and reinforcement learning.

Overall, the book provides a valuable resource for researchers and practitioners working in the field of distributed machine learning. It offers a deep dive into the various optimization algorithms that are used in distributed settings, helping readers understand the challenges and opportunities in this rapidly evolving field.

If you are interested in learning more about optimization algorithms for distributed machine learning, be sure to check out this insightful book. It is a must-read for anyone looking to enhance their knowledge and skills in this area.
#Optimization #Algorithms #Distributed #Machine #Learning #Synthesis #Lectures #Learning #Networks #Algorithms

December 24, 2024
Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax (Synthesis Lectures on Human Language Technologies)

Price: ~~$59.99~~ – $35.53
(as of Dec 24,2024 21:15:43 UTC – Details)

Publisher ‏ : ‎ Springer; 1st edition (June 18, 2013)
Language ‏ : ‎ English
Paperback ‏ : ‎ 188 pages
ISBN-10 ‏ : ‎ 3031010221
ISBN-13 ‏ : ‎ 978-3031010224
Item Weight ‏ : ‎ 11.7 ounces
Dimensions ‏ : ‎ 7.52 x 0.43 x 9.25 inches

Customers say

Customers find this book provides a comprehensive overview of linguistics for beginners and students. It is easy to read and covers essential principles for working in CL. The book is described as an excellent starting point for students and beginners.

AI-generated from the text of customer reviews

Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax (Synthesis Lectures on Human Language Technologies)

In the field of Natural Language Processing (NLP), having a solid understanding of linguistic fundamentals is essential for developing effective language processing algorithms. This book, “Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax,” provides a comprehensive overview of key concepts in morphology and syntax that are crucial for NLP applications.

Authored by experts in the field, this book covers a wide range of topics, including word formation, inflection, syntactic structures, and more. Each essential concept is explained in a clear and concise manner, making it accessible to both beginners and experienced researchers in the field.

Whether you are a researcher, developer, or student interested in NLP, this book serves as a valuable resource for understanding the linguistic foundations that underlie natural language processing systems. With practical examples and insights, readers will gain a deeper appreciation for the role that morphology and syntax play in shaping language processing technologies.

If you are looking to enhance your knowledge of linguistic fundamentals in NLP, this book is a must-read. Stay ahead of the curve and dive into the world of language technologies with “Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax.”
#Linguistic #Fundamentals #Natural #Language #Processing #Essentials #Morphology #Syntax #Synthesis #Lectures #Human #Language #Technologies

December 24, 2024
Explainable Machine Learning in Medicine (Synthesis Lectures on Engineering, Science, and Technology)

Price: ~~$89.99~~ – $85.49
(as of Dec 24,2024 20:58:27 UTC – Details)

Publisher ‏ : ‎ Springer (December 15, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 100 pages
ISBN-10 ‏ : ‎ 3031448790
ISBN-13 ‏ : ‎ 978-3031448799
Item Weight ‏ : ‎ 8.2 ounces
Dimensions ‏ : ‎ 6.61 x 0.23 x 9.45 inches

Explainable Machine Learning in Medicine: A Comprehensive Guide

Machine learning algorithms have shown great promise in revolutionizing the field of medicine, from diagnosing diseases to predicting patient outcomes. However, one major challenge in adopting these algorithms in clinical practice is their lack of transparency and interpretability. This is where explainable machine learning comes in.

In the book “Explainable Machine Learning in Medicine” from the Synthesis Lectures on Engineering, Science, and Technology series, authors delve into the importance of explainability in healthcare and provide insights into how machine learning models can be made more interpretable for physicians and healthcare professionals.

The book covers various topics, including:

– The importance of explainability in healthcare: The authors explain why it is crucial for machine learning models to be transparent and interpretable in medical settings, where decisions can have life-changing consequences.

– Techniques for making machine learning models explainable: The book explores different methods and approaches for improving the interpretability of machine learning models, such as feature importance analysis, model-agnostic explanations, and rule-based models.

– Case studies and real-world applications: The authors showcase examples of how explainable machine learning has been successfully applied in healthcare, from predicting patient readmissions to identifying high-risk individuals for preventive interventions.

Overall, “Explainable Machine Learning in Medicine” serves as a comprehensive guide for healthcare professionals, data scientists, and researchers interested in harnessing the power of machine learning in medicine while ensuring accountability and transparency. By making machine learning models explainable, we can build trust in these technologies and ultimately improve patient care and outcomes.
#Explainable #Machine #Learning #Medicine #Synthesis #Lectures #Engineering #Science #Technology

December 24, 2024
Unified Theories of Cognition (The William James Lectures)

Price: $29.91
(as of Dec 24,2024 20:36:43 UTC – Details)

Publisher ‏ : ‎ Harvard University Press; First Edition (January 1, 1990)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 576 pages
ISBN-10 ‏ : ‎ 0674920996
ISBN-13 ‏ : ‎ 978-0674920996
Item Weight ‏ : ‎ 2.12 pounds
Dimensions ‏ : ‎ 6.5 x 1.5 x 9.75 inches

In this post, we will delve into the fascinating world of cognition and explore the concept of Unified Theories of Cognition, as discussed in The William James Lectures.

Cognition, the mental process of acquiring knowledge and understanding, has been a subject of interest and debate for centuries. From the early philosophical musings of Aristotle and Descartes to the modern scientific studies of cognitive psychology and neuroscience, researchers have sought to unravel the mysteries of how the mind works.

The idea of Unified Theories of Cognition proposes that there may be a single, overarching framework that can explain all aspects of mental processes, from perception and attention to memory and decision-making. This concept challenges the traditional view that cognition is made up of separate, independent modules or systems.

The William James Lectures, named after the pioneering American psychologist and philosopher William James, have been a platform for leading researchers in the field of cognitive science to present their latest findings and theories. These lectures have shed light on the complex interplay between different cognitive processes and how they may be integrated into a unified theory.

Through a multidisciplinary approach that combines insights from psychology, neuroscience, computer science, and philosophy, researchers are working towards a more comprehensive understanding of cognition. By studying how the brain processes information, how we perceive the world around us, and how we make decisions, scientists are piecing together the puzzle of human cognition.

Unified Theories of Cognition offer a promising avenue for advancing our understanding of the mind and potentially revolutionizing how we approach education, artificial intelligence, and mental health. As we continue to unravel the complexities of cognition, The William James Lectures serve as a beacon of knowledge and inspiration for researchers and enthusiasts alike.
#Unified #Theories #Cognition #William #James #Lectures

December 24, 2024
Neural Network Methods for Natural Language Processing (Synthesis Lectures on Human Language Technologies)

Price: ~~$69.99~~ – $47.78
(as of Dec 24,2024 19:52:17 UTC – Details)

Publisher ‏ : ‎ Springer; 1st edition (April 17, 2017)
Language ‏ : ‎ English
Paperback ‏ : ‎ 312 pages
ISBN-10 ‏ : ‎ 303101037X
ISBN-13 ‏ : ‎ 978-3031010378
Item Weight ‏ : ‎ 1.19 pounds
Dimensions ‏ : ‎ 7.52 x 0.71 x 9.25 inches

Customers say

Customers find the book easy to read and well-organized. They consider it a good introduction to modern NLP and an interesting reference. However, opinions differ on the introduction, with some finding it useful and interesting as a reference, while others feel the content is basic and elementary.

AI-generated from the text of customer reviews

Neural Network Methods for Natural Language Processing (Synthesis Lectures on Human Language Technologies)

In recent years, neural network methods have revolutionized the field of natural language processing (NLP), enabling significant advancements in tasks such as machine translation, sentiment analysis, and question answering. This book, part of the Synthesis Lectures on Human Language Technologies series, provides a comprehensive overview of the application of neural network methods to NLP.

The book begins by introducing the fundamentals of neural networks and their use in NLP tasks. It then delves into advanced topics such as sequence-to-sequence models, attention mechanisms, and transformer architectures. Each chapter is accompanied by practical examples and case studies to help readers understand how these techniques are applied in real-world scenarios.

Written by leading experts in the field, this book is an essential resource for researchers, practitioners, and students interested in exploring the intersection of neural networks and natural language processing. Whether you are new to the field or looking to deepen your understanding of cutting-edge techniques, this book will provide valuable insights and guidance.
#Neural #Network #Methods #Natural #Language #Processing #Synthesis #Lectures #Human #Language #Technologies

December 24, 2024
Reasoning Web. Explainable Artificial Intelligence: 15th International Summer School 2019, Bolzano, Italy, September 20–24, 2019, Tutorial Lectures … Applications, incl. Internet/Web, and HCI)

Price: ~~$64.99~~ – $60.60
(as of Dec 24,2024 18:18:12 UTC – Details)

Publisher ‏ : ‎ Springer; 1st ed. 2019 edition (September 18, 2019)
Language ‏ : ‎ English
Paperback ‏ : ‎ 296 pages
ISBN-10 ‏ : ‎ 3030314227
ISBN-13 ‏ : ‎ 978-3030314224
Item Weight ‏ : ‎ 14.7 ounces
Dimensions ‏ : ‎ 6.1 x 0.67 x 9.25 inches

Reasoning Web. Explainable Artificial Intelligence: 15th International Summer School 2019, Bolzano, Italy, September 20–24, 2019, Tutorial Lectures

The 15th International Summer School on Reasoning Web and Explainable Artificial Intelligence will be held in Bolzano, Italy from September 20-24, 2019. This event will feature tutorial lectures on a wide range of topics related to artificial intelligence, including applications in Internet/Web and Human-Computer Interaction (HCI).

The goal of the summer school is to bring together researchers and practitioners in the field of artificial intelligence to explore the latest advancements in reasoning and explainable AI. Participants will have the opportunity to learn from experts in the field through a series of lectures, workshops, and hands-on exercises.

Topics covered in the tutorial lectures will include:

– Knowledge representation and reasoning
– Logic-based approaches to AI
– Machine learning and deep learning techniques
– Natural language processing and understanding
– Explainable AI and transparency in decision-making
– Applications of AI in Internet/Web and HCI

The summer school is open to students, researchers, and professionals who are interested in deepening their understanding of artificial intelligence and its applications. Whether you are a beginner or an experienced practitioner, this event offers a valuable opportunity to learn from leading experts in the field and network with peers from around the world.

Don’t miss this chance to expand your knowledge and skills in artificial intelligence at the Reasoning Web. Explainable Artificial Intelligence summer school in Bolzano, Italy. Register now and secure your spot for a week of insightful lectures, engaging workshops, and collaborative learning experiences.
#Reasoning #Web #Explainable #Artificial #Intelligence #15th #International #Summer #School #Bolzano #Italy #September #Tutorial #Lectures #Applications #incl #InternetWeb #HCI

December 24, 2024
Explainable Human-ai Interaction: A Planning Perspective (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Price: $49.49
(as of Dec 24,2024 14:50:23 UTC – Details)

Publisher ‏ : ‎ Morgan & Claypool (January 24, 2022)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 184 pages
ISBN-10 ‏ : ‎ 1636392911
ISBN-13 ‏ : ‎ 978-1636392912
Item Weight ‏ : ‎ 1.74 pounds

In today’s fast-paced world, the interaction between humans and artificial intelligence (AI) systems plays a crucial role in various aspects of our lives. From virtual assistants to autonomous vehicles, AI has become an integral part of our daily routines. However, the lack of transparency and interpretability in AI systems often leads to skepticism and distrust among users.

In the book “Explainable Human-AI Interaction: A Planning Perspective,” the authors delve into the concept of explainability in AI systems from a planning perspective. They explore how AI systems can be designed to provide clear and understandable explanations for their decisions and actions, enhancing trust and collaboration between humans and machines.

Drawing on insights from the fields of artificial intelligence and machine learning, the book offers a comprehensive overview of the latest research and techniques in explainable AI. It discusses the importance of incorporating human factors into AI systems, such as user preferences, cognitive biases, and decision-making processes.

By providing a planning perspective on explainable human-AI interaction, the book aims to bridge the gap between AI developers and end-users, enabling more effective and transparent communication between humans and machines. Whether you are a researcher, developer, or end-user of AI systems, this book is a valuable resource for understanding and improving the interaction between humans and AI.
#Explainable #Humanai #Interaction #Planning #Perspective #Synthesis #Lectures #Artificial #Intelligence #Machine #Learning

December 24, 2024
Graph-Based Semi-Supervised Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning, 29)

Price: ~~$40.00~~ – $34.83
(as of Dec 24,2024 14:30:42 UTC – Details)

Publisher ‏ : ‎ Morgan & Claypool Publishers (November 1, 2014)
Language ‏ : ‎ English
Paperback ‏ : ‎ 126 pages
ISBN-10 ‏ : ‎ 1627052011
ISBN-13 ‏ : ‎ 978-1627052016
Item Weight ‏ : ‎ 8.5 ounces
Dimensions ‏ : ‎ 7.5 x 0.29 x 9.25 inches

Graph-Based Semi-Supervised Learning: A Comprehensive Guide (Synthesis Lectures on Artificial Intelligence and Machine Learning, 29)

In the world of machine learning, semi-supervised learning is a powerful technique that leverages both labeled and unlabeled data to improve model performance. One particular approach that has gained popularity in recent years is graph-based semi-supervised learning.

In our latest edition of the Synthesis Lectures on Artificial Intelligence and Machine Learning, we delve into the intricacies of graph-based semi-supervised learning. This comprehensive guide covers the fundamentals of graph theory, the principles of semi-supervised learning, and how these two concepts intersect to create effective machine learning models.

With contributions from leading experts in the field, this book provides a thorough overview of the latest advancements in graph-based semi-supervised learning. Readers will gain insights into the different types of graphs used in machine learning, the algorithms that drive graph-based semi-supervised learning, and real-world applications of this technique.

Whether you’re a seasoned machine learning practitioner or a newcomer to the field, this book offers valuable insights and practical guidance on how to leverage graph-based semi-supervised learning for improved model performance. Stay ahead of the curve and pick up your copy of Graph-Based Semi-Supervised Learning today!
#GraphBased #SemiSupervised #Learning #Synthesis #Lectures #Artificial #Intelligence #Machine #Learning

December 24, 2024
Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Price: ~~$32.99~~ – $25.17
(as of Dec 24,2024 09:20:48 UTC – Details)

Publisher ‏ : ‎ Springer; 1st edition (July 7, 2010)
Language ‏ : ‎ English
Paperback ‏ : ‎ 104 pages
ISBN-10 ‏ : ‎ 303100423X
ISBN-13 ‏ : ‎ 978-3031004230
Item Weight ‏ : ‎ 6.9 ounces
Dimensions ‏ : ‎ 7.52 x 0.24 x 9.25 inches

Algorithms for Reinforcement Learning: A Comprehensive Guide

Reinforcement learning is a powerful approach to teaching machines how to make decisions and take actions in an uncertain environment. It has been successfully applied to a wide range of tasks, from playing games like chess and Go to controlling autonomous vehicles and robots.

In this post, we will explore some of the most important algorithms for reinforcement learning, as discussed in the book “Algorithms for Reinforcement Learning” by Csaba Szepesvari. This book is part of the Synthesis Lectures on Artificial Intelligence and Machine Learning series, which provides in-depth coverage of cutting-edge research in these fields.

The book covers a variety of algorithms for reinforcement learning, including value iteration, policy iteration, Q-learning, and deep reinforcement learning. These algorithms differ in their approaches to learning and decision-making, but they all share the goal of maximizing a cumulative reward over time.

One of the key concepts in reinforcement learning is the idea of exploration versus exploitation. In order to learn the optimal policy, an agent must explore different actions and their outcomes, while also exploiting the actions that have already been learned to be successful. Balancing exploration and exploitation is a challenging problem in reinforcement learning, and the book provides insights into how different algorithms address this issue.

Overall, “Algorithms for Reinforcement Learning” is a valuable resource for researchers, practitioners, and students interested in the field of reinforcement learning. Whether you are new to the topic or looking to deepen your understanding of advanced algorithms, this book is a must-read for anyone working in artificial intelligence and machine learning.
#Algorithms #Reinforcement #Learning #Synthesis #Lectures #Artificial #Intelligence #Machine #Learning

December 24, 2024
A Guide to Convolutional Neural Networks for Computer Vision (Synthesis Lectures

A Guide to Convolutional Neural Networks for Computer Vision (Synthesis Lectures

Price : 39.44

Ends on : N/A

View on eBay
Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision, enabling machines to recognize patterns and objects in images with impressive accuracy. In this guide, we will provide a comprehensive overview of CNNs for computer vision, explaining their architecture, training process, and applications.

1. Introduction to CNNs:
– CNNs are a type of deep neural network designed specifically for processing visual data, such as images and videos.
– They are composed of multiple layers, including convolutional layers, pooling layers, and fully connected layers.
– Convolutional layers apply filters to the input image, extracting features like edges, textures, and shapes.
– Pooling layers reduce the spatial dimensions of the feature maps, helping to make the network more computationally efficient.

2. Training CNNs:
– CNNs are typically trained using a technique called backpropagation, where the network learns to adjust its parameters based on the error between the predicted and actual outputs.
– Training data is fed into the network, and the weights of each layer are updated iteratively to minimize the loss function.
– Common optimization algorithms used for training CNNs include Stochastic Gradient Descent (SGD), Adam, and RMSprop.

3. Applications of CNNs in Computer Vision:
– CNNs have been successfully applied to a wide range of computer vision tasks, including image classification, object detection, and image segmentation.
– Image classification involves assigning a label to an input image based on its contents, such as identifying whether it contains a cat or a dog.
– Object detection aims to locate and classify objects within an image, enabling tasks like autonomous driving and surveillance.
– Image segmentation involves partitioning an image into different regions or objects, allowing for more detailed analysis and understanding.

In conclusion, CNNs have become a powerful tool for computer vision, enabling machines to perceive and interpret visual data with human-like accuracy. By understanding the architecture, training process, and applications of CNNs, you can leverage this technology to develop innovative solutions in various domains.
#Guide #Convolutional #Neural #Networks #Computer #Vision #Synthesis #Lectures

December 24, 2024

Hello, how can I help you today?

Gathering thoughts.. ...