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Tag: Probabilistic

  • Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability

    Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability


    Price: $49.99 – $43.84
    (as of Dec 24,2024 10:12:47 UTC – Details)




    Publisher ‏ : ‎ Manning; 1st edition (November 10, 2020)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 296 pages
    ISBN-10 ‏ : ‎ 1617296074
    ISBN-13 ‏ : ‎ 978-1617296079
    Item Weight ‏ : ‎ 1.1 pounds
    Dimensions ‏ : ‎ 7.38 x 0.3 x 9.25 inches


    Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability

    In the world of deep learning, there is a growing interest in incorporating uncertainty into neural networks. This is where probabilistic deep learning comes into play, allowing models to not only make predictions, but also quantify the uncertainty around those predictions.

    One popular framework for implementing probabilistic deep learning is TensorFlow Probability, which extends TensorFlow with tools for building and training probabilistic models. Paired with Keras, a high-level neural networks API, you can easily create and train probabilistic models in Python.

    In this post, we will explore how to implement probabilistic deep learning using Python, Keras, and TensorFlow Probability. We will cover topics such as defining probabilistic layers, training models with uncertainty, and evaluating model performance using probabilistic metrics.

    By the end of this post, you will have a solid understanding of how to leverage probabilistic deep learning techniques in your own projects, allowing you to build more robust and reliable models that can account for uncertainty in their predictions. So stay tuned for an in-depth dive into the world of probabilistic deep learning with Python, Keras, and TensorFlow Probability.
    #Probabilistic #Deep #Learning #Python #Keras #TensorFlow #Probability

  • Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks (Information Science and Statistics)

    Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks (Information Science and Statistics)


    Price: $129.99 – $60.22
    (as of Dec 24,2024 09:17:38 UTC – Details)




    Publisher ‏ : ‎ Springer; 1999th edition (July 16, 2007)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 336 pages
    ISBN-10 ‏ : ‎ 0387718230
    ISBN-13 ‏ : ‎ 978-0387718231
    Item Weight ‏ : ‎ 2.31 pounds
    Dimensions ‏ : ‎ 6.1 x 0.76 x 9.25 inches


    In this post, we will discuss the book “Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks (Information Science and Statistics)” by Daphne Koller and Nir Friedman.

    This comprehensive book provides an in-depth look at exact computational methods for Bayesian networks, which are powerful tools for modeling and analyzing complex probabilistic relationships. The authors, both experts in the field, cover a wide range of topics including inference algorithms, learning techniques, and applications of Bayesian networks.

    The book is aimed at researchers, students, and practitioners in fields such as artificial intelligence, machine learning, and statistics. It provides a thorough introduction to the principles and techniques of Bayesian networks, as well as practical guidance on how to implement and use these methods in real-world applications.

    Overall, “Probabilistic Networks and Expert Systems” is a valuable resource for anyone interested in understanding and utilizing Bayesian networks for probabilistic reasoning and decision making. It is a must-read for those looking to deepen their knowledge and skills in this important area of information science and statistics.
    #Probabilistic #Networks #Expert #Systems #Exact #Computational #Methods #Bayesian #Networks #Information #Science #Statistics

  • Expert Systems and Probabilistic Network Models (Monographs in Computer science)

    Expert Systems and Probabilistic Network Models (Monographs in Computer science)



    Expert Systems and Probabilistic Network Models (Monographs in Computer science)

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    Expert Systems and Probabilistic Network Models (Monographs in Computer Science)

    Are you interested in learning more about expert systems and probabilistic network models in computer science? Look no further than this comprehensive monograph, which delves into the intricacies of these advanced technologies.

    Expert systems are AI systems that mimic the decision-making abilities of a human expert in a specific domain. These systems use knowledge bases and inference engines to provide expert-level advice and recommendations. Probabilistic network models, on the other hand, are used to represent and reason about uncertainty in a system. By combining these two technologies, researchers and practitioners can create powerful tools for solving complex problems in various domains.

    This monograph covers the theoretical foundations of expert systems and probabilistic network models, as well as practical applications in areas such as healthcare, finance, and cybersecurity. It also explores cutting-edge research in the field, highlighting the latest advancements and future directions.

    Whether you are a seasoned professional looking to expand your knowledge or a student interested in exploring the cutting-edge of computer science, this monograph is a must-read. Get your copy today and unlock the potential of expert systems and probabilistic network models in your work.
    #Expert #Systems #Probabilistic #Network #Models #Monographs #Computer #science

  • Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machin

    Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machin



    Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machin

    Price : 89.77

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    e Learning Series)

    Probabilistic Machine Learning: An Introduction

    Machine learning has become an essential tool in various domains such as healthcare, finance, and autonomous driving. One of the key challenges in machine learning is dealing with uncertainty in the data and making predictions based on this uncertainty. Probabilistic machine learning offers a framework to address this challenge by incorporating probability theory into machine learning models.

    In this post, we will provide an introduction to probabilistic machine learning, focusing on its principles and applications. We will also discuss the Adaptive Computation and Machine Learning Series, which provides a comprehensive overview of probabilistic machine learning techniques.

    Probabilistic machine learning is based on the idea that data is inherently uncertain, and that predictions should be made in a probabilistic manner. This means that instead of providing a single point estimate for a prediction, probabilistic machine learning models provide a probability distribution over possible outcomes. This allows for more robust and interpretable predictions, as well as quantifying the uncertainty in the predictions.

    The Adaptive Computation and Machine Learning Series is a collection of books that cover various topics in probabilistic machine learning, including Bayesian inference, Gaussian processes, and variational inference. These books provide a solid foundation in probabilistic machine learning techniques, and are suitable for both beginners and experienced practitioners.

    In conclusion, probabilistic machine learning offers a powerful framework for dealing with uncertainty in machine learning models. The Adaptive Computation and Machine Learning Series is a valuable resource for those looking to deepen their understanding of probabilistic machine learning techniques. Stay tuned for more posts on probabilistic machine learning and its applications!
    #Probabilistic #Machine #Learning #Introduction #Adaptive #Computation #Machin

  • Probabilistic Deep Learning with Python – Paperback By Duerr, Oliver – VERY GOOD

    Probabilistic Deep Learning with Python – Paperback By Duerr, Oliver – VERY GOOD



    Probabilistic Deep Learning with Python – Paperback By Duerr, Oliver – VERY GOOD

    Price : 37.36

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    Probabilistic Deep Learning with Python – Paperback By Duerr, Oliver – VERY GOOD

    Looking to dive into the world of probabilistic deep learning with Python? Look no further than Oliver Duerr’s comprehensive guide, “Probabilistic Deep Learning with Python.” This book provides a thorough introduction to the concepts and techniques of probabilistic deep learning, with a focus on practical applications using Python.

    With a clear and concise writing style, Duerr guides readers through the fundamentals of probabilistic modeling, Bayesian inference, and deep learning. The book includes hands-on examples and exercises to help reinforce key concepts and deepen understanding.

    Whether you’re a beginner looking to learn the basics of probabilistic deep learning or an experienced practitioner seeking to enhance your skills, “Probabilistic Deep Learning with Python” is a valuable resource that will help you take your knowledge to the next level.

    Don’t miss out on this opportunity to learn from one of the leading experts in the field. Pick up your copy of “Probabilistic Deep Learning with Python” today and start your journey towards mastering probabilistic deep learning.
    #Probabilistic #Deep #Learning #Python #Paperback #Duerr #Oliver #GOOD

  • Probabilistic Machine Learning: An Introduction (Adaptive Computation and – GOOD

    Probabilistic Machine Learning: An Introduction (Adaptive Computation and – GOOD



    Probabilistic Machine Learning: An Introduction (Adaptive Computation and – GOOD

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    Probabilistic Machine Learning: An Introduction (Adaptive Computation and Learning Series)

    Machine learning has revolutionized the way we approach complex problems in various fields such as healthcare, finance, and natural language processing. One of the key branches of machine learning is probabilistic machine learning, which offers a powerful framework for modeling uncertainty and making decisions in uncertain environments.

    In this post, we will provide an introduction to probabilistic machine learning, focusing on the principles and algorithms that underlie this approach. We will also discuss the Adaptive Computation and Learning Series, a collection of books that cover advanced topics in machine learning and artificial intelligence.

    Probabilistic machine learning is based on the idea of modeling data using probability distributions. By incorporating uncertainty into the modeling process, probabilistic machine learning algorithms can make more robust predictions and decisions. This approach is particularly useful in situations where data is noisy, incomplete, or ambiguous.

    One of the key advantages of probabilistic machine learning is its ability to provide not only predictions but also estimates of uncertainty. This can be crucial in applications where understanding the confidence of a prediction is just as important as the prediction itself. Additionally, probabilistic machine learning can handle small amounts of data more effectively than traditional machine learning approaches.

    The Adaptive Computation and Learning Series is a valuable resource for those looking to dive deeper into the world of machine learning and artificial intelligence. The series covers a wide range of topics, including probabilistic modeling, neural networks, and reinforcement learning. Each book in the series is written by experts in the field, making it a reliable source of information for both beginners and advanced practitioners.

    In conclusion, probabilistic machine learning offers a powerful framework for modeling uncertainty and making decisions in complex environments. By understanding the principles and algorithms behind probabilistic machine learning, you can unlock new possibilities in your own machine learning projects. Check out the Adaptive Computation and Learning Series for more in-depth information on this exciting field.
    #Probabilistic #Machine #Learning #Introduction #Adaptive #Computation #GOOD

  • Probabilistic Machine Learning: An Introduction (Adaptive Computation and Mac…

    Probabilistic Machine Learning: An Introduction (Adaptive Computation and Mac…



    Probabilistic Machine Learning: An Introduction (Adaptive Computation and Mac…

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    Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)

    In the world of machine learning, probabilistic methods have gained increasing popularity due to their ability to handle uncertainty and make more reliable predictions. Probabilistic machine learning combines the principles of probability theory with the power of machine learning algorithms to create models that can make informed decisions based on incomplete or noisy data.

    In this post, we will provide an introduction to probabilistic machine learning, focusing on its key concepts and applications. We will also explore how probabilistic models can be used to improve the performance of machine learning systems in various domains.

    Stay tuned for a deep dive into the world of probabilistic machine learning and how it is revolutionizing the field of artificial intelligence.
    #Probabilistic #Machine #Learning #Introduction #Adaptive #Computation #Mac..

  • Probabilistic Machine Learning: An Introduction (Adaptive Computation and Mac…

    Probabilistic Machine Learning: An Introduction (Adaptive Computation and Mac…



    Probabilistic Machine Learning: An Introduction (Adaptive Computation and Mac…

    Price : 190.00

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    Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)

    In the world of machine learning, probabilistic models are gaining increasing popularity due to their ability to provide uncertainty estimates in predictions. These models not only make predictions based on data but also quantify the uncertainty in those predictions, which is crucial in many real-world applications.

    Probabilistic machine learning is a subfield of machine learning that combines the principles of probability theory with the power of machine learning algorithms. By incorporating uncertainty into the modeling process, probabilistic machine learning can make more robust and reliable predictions, especially in situations where data is limited or noisy.

    In the book “Probabilistic Machine Learning: An Introduction” from the Adaptive Computation and Machine Learning series, readers will learn the fundamentals of probabilistic modeling and how to apply these techniques to real-world problems. The book covers topics such as Bayesian inference, probabilistic graphical models, and variational inference, providing a comprehensive overview of probabilistic machine learning methods.

    Whether you are a beginner looking to learn the basics of probabilistic modeling or an experienced practitioner wanting to deepen your understanding of these techniques, this book is a valuable resource for anyone interested in the intersection of probability theory and machine learning. Dive into the world of probabilistic machine learning and discover how uncertainty can lead to more accurate and reliable predictions.
    #Probabilistic #Machine #Learning #Introduction #Adaptive #Computation #Mac..

  • Probabilistic Machine Learning – Paperback, by Kanungo Deepak K. – Acceptable

    Probabilistic Machine Learning – Paperback, by Kanungo Deepak K. – Acceptable



    Probabilistic Machine Learning – Paperback, by Kanungo Deepak K. – Acceptable

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    Probabilistic Machine Learning – Paperback, by Kanungo Deepak K. – Acceptable

    Looking for a comprehensive guide to understanding probabilistic machine learning? Look no further than Kanungo Deepak K.’s latest book, now available in paperback! This book covers all the essential concepts and techniques in probabilistic machine learning, making it an invaluable resource for both beginners and experienced practitioners.

    With clear explanations and practical examples, Kanungo Deepak K. breaks down complex topics into easy-to-understand concepts, making it accessible to readers of all levels. Whether you’re looking to enhance your knowledge of machine learning or dive into probabilistic modeling, this book has got you covered.

    Get your hands on Probabilistic Machine Learning – Paperback today and take your understanding of machine learning to the next level!
    #Probabilistic #Machine #Learning #Paperback #Kanungo #Deepak #Acceptable

  • Probabilistic Deep Learning: With – Paperback, by Duerr Oliver; Sick – Good

    Probabilistic Deep Learning: With – Paperback, by Duerr Oliver; Sick – Good



    Probabilistic Deep Learning: With – Paperback, by Duerr Oliver; Sick – Good

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    Probabilistic Deep Learning: With – Paperback, by Duerr Oliver; Sick – Good

    Are you interested in diving deeper into the world of probabilistic deep learning? Look no further than “Probabilistic Deep Learning: With – Paperback” by Duerr Oliver. This comprehensive guide will take you through the fundamentals of this cutting-edge technology, providing you with the tools and knowledge needed to apply it in your own projects.

    With a focus on practical applications and real-world examples, this book is perfect for both beginners and experienced practitioners looking to expand their skill set. From understanding the basics of probabilistic modeling to implementing advanced deep learning techniques, you’ll find everything you need to succeed in this rapidly growing field.

    So why wait? Grab your copy of “Probabilistic Deep Learning: With – Paperback” by Duerr Oliver today and start your journey towards mastering probabilistic deep learning. Your future self will thank you!
    #Probabilistic #Deep #Learning #Paperback #Duerr #Oliver #Sick #Good