Your cart is currently empty!
Tag: Computation
Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
Price:$125.00– $110.59
(as of Dec 15,2024 18:10:57 UTC – Details)
Publisher : The MIT Press; Illustrated edition (August 24, 2012)
Language : English
Hardcover : 1104 pages
ISBN-10 : 0262018020
ISBN-13 : 978-0262018029
Item Weight : 2.31 pounds
Dimensions : 8.31 x 1.61 x 9.31 inches
Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)In the world of artificial intelligence and data science, machine learning is a powerful tool that allows computers to learn from data and make predictions or decisions without being explicitly programmed. One popular approach to machine learning is the probabilistic perspective, which views machine learning as a form of statistical inference where uncertainty is explicitly modeled using probability theory.
The book “Machine Learning: A Probabilistic Perspective” is an essential resource for anyone looking to dive deeper into this approach. Written by Kevin P. Murphy, a leading expert in the field, this book provides a comprehensive introduction to the probabilistic perspective on machine learning.
The book covers a wide range of topics, including Bayesian networks, hidden Markov models, Gaussian processes, and more. It also includes practical examples and exercises to help readers understand and apply the concepts discussed.
Whether you are a beginner looking to learn the basics of machine learning or an experienced practitioner seeking to deepen your understanding of the probabilistic perspective, this book is a must-read. Join the ranks of those who have benefited from this invaluable resource and take your machine learning skills to the next level.
#Machine #Learning #Probabilistic #Perspective #Adaptive #Computation #Machine #Learning #series
Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machin
Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machin
Price : 93.92
Ends on : N/A
View on eBay
e Learning)In this post, we will delve into advanced topics in probabilistic machine learning, specifically focusing on adaptive computation and machine learning algorithms.
Adaptive computation refers to the ability of a machine learning model to adapt and improve its performance over time based on incoming data. This is crucial in real-world applications where data is constantly changing and evolving.
One key concept in adaptive computation is online learning, where a model learns from each new data point in a sequential manner, rather than in batch mode. This allows the model to quickly adapt to changes in the data distribution and make decisions in real-time.
Another important aspect of adaptive computation is active learning, where the model actively selects which data points to label in order to maximize its learning efficiency. By focusing on the most informative data points, the model can reduce the amount of labeled data required for training, leading to faster convergence and improved performance.
In terms of machine learning algorithms, probabilistic models such as Gaussian processes and Bayesian neural networks are well-suited for adaptive computation. These models provide a principled way to incorporate uncertainty into the learning process, allowing for more robust and reliable predictions.
Overall, adaptive computation and machine learning algorithms play a crucial role in advancing the field of probabilistic machine learning. By continuously adapting and improving, these models are able to handle complex and dynamic data environments, making them invaluable tools for a wide range of applications.
#Probabilistic #Machine #Learning #Advanced #Topics #Adaptive #Computation #Machin
Introduction to Machine Learning, fourth edition (Adaptive Computation and Machine Learning series)
Price:$85.00– $55.67
(as of Dec 02,2024 15:55:12 UTC – Details)
Publisher : The MIT Press; 4th edition (March 24, 2020)
Language : English
Hardcover : 712 pages
ISBN-10 : 0262043793
ISBN-13 : 978-0262043793
Item Weight : 2.31 pounds
Dimensions : 8.31 x 1.46 x 9.38 inches
Introduction to Machine Learning, fourth edition (Adaptive Computation and Machine Learning series)Machine learning is a rapidly growing field that is revolutionizing the way we approach problems in various industries. Whether it’s predicting customer behavior, optimizing supply chains, or even developing self-driving cars, machine learning has become an essential tool for businesses and researchers alike.
The fourth edition of “Introduction to Machine Learning” is a comprehensive guide to the fundamentals of machine learning, written by renowned experts in the field. This book covers a wide range of topics, including supervised and unsupervised learning, reinforcement learning, and deep learning.
With a focus on practical applications and real-world examples, this book is perfect for beginners looking to get started in machine learning, as well as experienced professionals looking to deepen their knowledge. The Adaptive Computation and Machine Learning series provides a solid foundation for understanding the core concepts of machine learning, making it an essential resource for anyone interested in this rapidly evolving field.
Whether you’re a student, researcher, or industry professional, “Introduction to Machine Learning” is a must-have reference that will help you stay ahead in the world of artificial intelligence and data science.
#Introduction #Machine #Learning #fourth #edition #Adaptive #Computation #Machine #Learning #series
Adaptive Computation and Machine Learning Ser.: Reinforcement Learning,…
Adaptive Computation and Machine Learning Ser.: Reinforcement Learning,…
Price : 39.95
Ends on : N/A
View on eBay
In this post, we will delve into the world of Reinforcement Learning, a key topic in the field of Adaptive Computation and Machine Learning. Reinforcement Learning is a type of machine learning where an agent learns to make decisions by interacting with its environment and receiving feedback in the form of rewards or penalties.We will explore the basic concepts of Reinforcement Learning, including the notion of an agent, environment, actions, rewards, and the goal of maximizing cumulative rewards over time. We will also discuss some common algorithms used in Reinforcement Learning, such as Q-learning, Deep Q Networks (DQN), and Policy Gradient methods.
Additionally, we will touch upon some real-world applications of Reinforcement Learning, such as playing games (e.g., AlphaGo), robotic control, and autonomous driving. We will also discuss some of the challenges and limitations of Reinforcement Learning and potential future research directions in this exciting and rapidly evolving field.
So, if you’re interested in learning more about Reinforcement Learning and its applications in Adaptive Computation and Machine Learning, stay tuned for our upcoming posts in this series!
#Adaptive #Computation #Machine #Learning #Ser #Reinforcement #Learning..
Intelligent Engineering Systems Through Artificial Neural Networks, Volume 16: Smart Engineering System Design: Infra-Structure Systems Engineering, Bio-Informatics and Computational Biology, and Evolutionary Computation
Price:$218.00– $184.54
(as of Nov 27,2024 08:23:21 UTC – Details)
Publisher : American Society of Mechanical Engineers; Illustrated edition (January 1, 2006)
Language : English
Hardcover : 857 pages
ISBN-10 : 0791802566
ISBN-13 : 978-0791802564
Item Weight : 2.8 pounds
Dimensions : 5.9 x 1.9 x 9.3 inches
Introducing Volume 16 of Intelligent Engineering Systems Through Artificial Neural Networks: Smart Engineering System Design. This latest volume focuses on Infra-Structure Systems Engineering, Bio-Informatics and Computational Biology, and Evolutionary Computation, offering cutting-edge insights into the intersection of intelligent engineering and advanced technologies.In this volume, experts in the field explore the application of artificial neural networks in designing smart engineering systems, with a particular focus on infra-structure systems engineering. From optimizing transportation networks to enhancing energy efficiency in buildings, the possibilities for intelligent engineering systems are endless.
Additionally, this volume delves into the exciting realms of bio-informatics and computational biology, showcasing how artificial neural networks can revolutionize the study of biological systems. By leveraging the power of neural networks, researchers can uncover new insights into complex biological processes and drive innovations in healthcare and pharmaceuticals.
Lastly, Volume 16 also explores the role of evolutionary computation in smart engineering system design. By mimicking the principles of natural selection and genetic algorithms, researchers can optimize engineering systems and solve complex optimization problems with unparalleled efficiency.
Overall, Smart Engineering System Design offers a comprehensive look at the transformative potential of artificial neural networks in engineering and technology. Whether you’re a researcher, engineer, or technology enthusiast, this volume is a must-read for anyone interested in the future of intelligent engineering systems.
#Intelligent #Engineering #Systems #Artificial #Neural #Networks #Volume #Smart #Engineering #System #Design #InfraStructure #Systems #Engineering #BioInformatics #Computational #Biology #Evolutionary #Computation
Cloud Computing for Science and Engineering (Scientific and Engineering Computation)
Price:$55.00– $27.24
(as of Nov 26,2024 12:57:45 UTC – Details)
ASIN : 0262037246
Publisher : The MIT Press; 1st edition (September 29, 2017)
Language : English
Hardcover : 392 pages
ISBN-10 : 9780262037242
ISBN-13 : 978-0262037242
Reading age : 18 years and up
Grade level : 12 and up
Item Weight : 2.26 pounds
Dimensions : 9.2 x 7.2 x 1.3 inches
Cloud Computing for Science and Engineering (Scientific and Engineering Computation)Cloud computing has revolutionized the way scientific and engineering computations are carried out. With the ability to access unlimited computing power and storage resources on-demand, researchers and engineers can now tackle complex problems more efficiently and effectively than ever before.
One of the key advantages of cloud computing for science and engineering is the ability to scale resources up or down based on the specific requirements of a project. This flexibility allows researchers to run simulations, analyze data, and perform computations with greater speed and accuracy, ultimately leading to faster results and discoveries.
Furthermore, cloud computing offers a cost-effective solution for organizations that may not have the budget or infrastructure to support large-scale computational tasks in-house. By leveraging cloud services, researchers and engineers can access high-performance computing resources without the need for expensive hardware investments.
Additionally, cloud computing enables collaboration and data sharing among researchers and teams located in different geographical locations. With cloud-based tools and platforms, scientists and engineers can easily collaborate on projects, share data, and access resources in real-time, leading to more efficient workflows and improved research outcomes.
Overall, cloud computing has transformed the landscape of scientific and engineering computation, offering new possibilities for innovation and discovery. As technology continues to evolve, it is clear that cloud computing will play an increasingly important role in advancing research and development in various fields of science and engineering.
#Cloud #Computing #Science #Engineering #Scientific #Engineering #Computation
Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machin
Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machin
Price : 74.47
Ends on : N/A
View on eBay
e Learning Series)In the world of artificial intelligence and machine learning, probabilistic machine learning is a powerful approach that allows machines to make decisions based on uncertain or incomplete information. By incorporating probabilistic models, machine learning algorithms can not only make predictions but also provide estimates of the uncertainty associated with those predictions.
In this post, we will provide an introduction to probabilistic machine learning, focusing on its applications, advantages, and challenges. We will also explore how probabilistic machine learning can be used to improve the performance of machine learning models in various domains.
Probabilistic machine learning is an essential tool for dealing with uncertainty in real-world data. By incorporating probabilistic models into machine learning algorithms, we can make more informed decisions and better understand the limitations of our models. Whether it’s predicting the likelihood of a customer purchasing a product, diagnosing a medical condition, or navigating a self-driving car through unpredictable traffic, probabilistic machine learning offers a flexible and powerful approach to solving complex problems.
Join us as we delve into the world of probabilistic machine learning and discover how this adaptive approach can revolutionize the field of artificial intelligence and machine learning.
#Probabilistic #Machine #Learning #Introduction #Adaptive #Computation #Machin
Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)
Price:$150.00– $142.33
(as of Nov 25,2024 17:07:16 UTC – Details)
Publisher : The MIT Press (August 15, 2023)
Language : English
Hardcover : 1360 pages
ISBN-10 : 0262048434
ISBN-13 : 978-0262048439
Item Weight : 2.31 pounds
Dimensions : 8.38 x 2.18 x 9.31 inches
Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)In the world of machine learning, probabilistic models have gained popularity due to their ability to handle uncertainty and make informed decisions based on probabilities. In this post, we will delve into advanced topics in probabilistic machine learning, focusing on the Adaptive Computation and Machine Learning series.
Probabilistic machine learning is a powerful framework that allows us to model complex relationships in data and make predictions while accounting for uncertainty. The Adaptive Computation and Machine Learning series offers a comprehensive guide to mastering advanced topics in this field, including Bayesian inference, Gaussian processes, and variational inference.
One key concept in probabilistic machine learning is Bayesian inference, which allows us to update our beliefs about a hypothesis as we observe new data. This framework is essential for building flexible and robust models that can adapt to changing environments.
Another important topic covered in the series is Gaussian processes, which are a versatile tool for modeling complex relationships in data. These non-parametric models offer a flexible way to capture dependencies between variables and make predictions with uncertainty estimates.
Lastly, the series explores variational inference, a powerful technique for approximating complex posterior distributions in probabilistic models. By optimizing a lower bound on the true posterior, variational inference allows us to efficiently learn the parameters of our model and make accurate predictions.
Overall, the Adaptive Computation and Machine Learning series provides a deep dive into advanced topics in probabilistic machine learning, offering a wealth of knowledge for researchers and practitioners alike. If you’re interested in mastering probabilistic modeling and making informed decisions based on uncertainty, this series is a must-read.
#Probabilistic #Machine #Learning #Advanced #Topics #Adaptive #Computation #Machine #Learning #seriesAdaptive Computation and Machine Learning Ser.: Deep Learning by Yoshua Bengio,
Adaptive Computation and Machine Learning Ser.: Deep Learning by Yoshua Bengio,
Price : 34.40
Ends on : N/A
View on eBay
Adaptive Computation and Machine Learning Ser.: Deep Learning by Yoshua BengioDeep Learning, written by renowned computer scientist Yoshua Bengio, is a groundbreaking book that delves into the world of artificial intelligence and machine learning. This book is part of the Adaptive Computation and Machine Learning series, which aims to provide in-depth insights into the latest advancements in the field.
In Deep Learning, Bengio explores the foundations of deep learning, a subfield of machine learning that focuses on creating algorithms inspired by the structure and function of the brain. The book covers a wide range of topics, including neural networks, deep neural networks, convolutional neural networks, and recurrent neural networks.
Bengio’s expertise in the field shines through in this comprehensive guide, making it a must-read for anyone interested in the future of artificial intelligence and machine learning. Whether you are a seasoned professional or a newcomer to the field, Deep Learning offers valuable insights and practical advice for mastering the complexities of deep learning algorithms.
If you’re looking to expand your knowledge of artificial intelligence and machine learning, Deep Learning by Yoshua Bengio is the perfect addition to your library. Dive into the world of deep learning and discover the endless possibilities that this cutting-edge technology has to offer.
#Adaptive #Computation #Machine #Learning #Ser #Deep #Learning #Yoshua #Bengio
Adaptive Computation and Machine Learnin Reinforcement Learning, Second Edition
Adaptive Computation and Machine Learnin Reinforcement Learning, Second Edition
Price : 62.78
Ends on : N/A
View on eBay
Adaptive Computation and Machine Learning: Reinforcement Learning, Second EditionReinforcement learning is a powerful technique that has revolutionized the field of artificial intelligence and machine learning. With the increasing demand for intelligent systems that can learn and adapt to their environments, understanding reinforcement learning has become essential for researchers and practitioners in the field.
The second edition of “Adaptive Computation and Machine Learning: Reinforcement Learning” provides a comprehensive and in-depth overview of the latest developments in reinforcement learning. Written by leading experts in the field, this book covers a wide range of topics, including deep reinforcement learning, multi-agent reinforcement learning, and reinforcement learning in continuous action spaces.
This updated edition also includes new chapters on topics such as meta-learning, imitation learning, and inverse reinforcement learning, making it an essential resource for anyone interested in understanding the cutting-edge techniques in reinforcement learning.
Whether you are a researcher, student, or practitioner in the field of artificial intelligence and machine learning, “Adaptive Computation and Machine Learning: Reinforcement Learning, Second Edition” is a must-have reference that will help you stay ahead of the curve in this rapidly evolving field.
#Adaptive #Computation #Machine #Learnin #Reinforcement #Learning #Edition
