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Tag: Probabilistic

  • Probabilistic Reasoning in Expert Systems: Theory and Algorithms by Neapolitan

    Probabilistic Reasoning in Expert Systems: Theory and Algorithms by Neapolitan



    Probabilistic Reasoning in Expert Systems: Theory and Algorithms by Neapolitan

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    Probabilistic Reasoning in Expert Systems: Theory and Algorithms by Neapolitan

    In the field of artificial intelligence, expert systems play a crucial role in making decisions and solving complex problems. One key aspect of expert systems is probabilistic reasoning, which allows for uncertainty to be incorporated into the decision-making process.

    In his book “Probabilistic Reasoning in Expert Systems: Theory and Algorithms,” Judea Pearl provides a comprehensive overview of the theoretical foundations and practical algorithms for incorporating probabilistic reasoning into expert systems. This book is a must-read for anyone interested in understanding how uncertainty can be effectively managed in decision-making processes.

    Neapolitan’s book delves into the mathematical principles behind probabilistic reasoning, such as Bayesian networks and Markov models, and provides practical examples and algorithms for implementing these concepts in expert systems. By understanding the theory and algorithms presented in this book, practitioners can enhance the capabilities of their expert systems and make more informed decisions in uncertain environments.

    Overall, “Probabilistic Reasoning in Expert Systems: Theory and Algorithms” is a valuable resource for researchers, practitioners, and students looking to deepen their understanding of probabilistic reasoning and its applications in expert systems. With its clear explanations and practical examples, this book is sure to be a valuable addition to any AI enthusiast’s library.
    #Probabilistic #Reasoning #Expert #Systems #Theory #Algorithms #Neapolitan

  • Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machin

    Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machin



    Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machin

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    e Learning)

    In this post, we will delve into advanced topics in probabilistic machine learning, focusing on adaptive computation and machine learning techniques.

    Adaptive computation refers to the ability of a machine learning model to dynamically adjust its computational resources based on the complexity of the data it is processing. This allows the model to efficiently handle large datasets and complex patterns without sacrificing accuracy.

    One popular technique for adaptive computation is the use of Bayesian optimization, which is a method for optimizing hyperparameters of machine learning models. By iteratively selecting hyperparameters based on their expected performance, Bayesian optimization can efficiently search through a large parameter space and find the optimal settings for a given model.

    Another key aspect of adaptive computation is the concept of ensemble learning, where multiple machine learning models are combined to improve predictive performance. Ensemble methods such as random forests and boosting can help mitigate the bias-variance tradeoff and improve the overall robustness of a machine learning model.

    In addition to adaptive computation, we will also explore advanced topics in machine learning such as generative adversarial networks (GANs) and deep reinforcement learning. GANs are a powerful class of models that can generate realistic images and text by training two neural networks in a competitive manner. Deep reinforcement learning, on the other hand, is a technique for training agents to make decisions in complex environments by interacting with the environment and receiving rewards.

    Overall, probabilistic machine learning is a rapidly evolving field with many advanced topics and techniques to explore. By understanding and applying these advanced concepts, we can build more robust and efficient machine learning models that can tackle a wide range of real-world problems.
    #Probabilistic #Machine #Learning #Advanced #Topics #Adaptive #Computation #Machin

  • Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine

    Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine



    Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine

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    Learning series)

    In this post, we will explore the concept of machine learning from a probabilistic perspective, focusing on the Adaptive Computation and Machine Learning series.

    Machine learning is a field of study that aims to develop algorithms and techniques that enable computers to learn from data and make predictions or decisions without being explicitly programmed. One popular approach to machine learning is through the use of probabilistic models, which allow for uncertainty in both the input data and the predictions made by the model.

    The Adaptive Computation and Machine Learning series, published by MIT Press, provides a comprehensive overview of the theory and practice of machine learning from a probabilistic perspective. The series covers a wide range of topics, including Bayesian inference, graphical models, and deep learning, and is suitable for both students and researchers in the field.

    By approaching machine learning from a probabilistic perspective, we are able to incorporate uncertainty into our models and make more robust predictions. This can be particularly useful in applications where the data is noisy or incomplete, such as in medical diagnosis or financial forecasting.

    Overall, the Adaptive Computation and Machine Learning series offers a valuable resource for anyone interested in exploring machine learning from a probabilistic viewpoint. With its comprehensive coverage of the theory and practical applications of probabilistic models, this series is sure to be a valuable addition to the library of any machine learning enthusiast.
    #Machine #Learning #Probabilistic #Perspective #Adaptive #Computation #Machine, machine learning

  • A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability)

    A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability)


    Price: $139.99 – $110.54
    (as of Dec 27,2024 10:32:34 UTC – Details)



    A Probabilistic Theory of Pattern Recognition: Exploring Stochastic Modelling and Applied Probability

    Pattern recognition is a fundamental problem in many fields, from computer vision to speech recognition to biology. While traditional methods of pattern recognition often rely on deterministic algorithms, a probabilistic approach can provide more robust and flexible solutions.

    In the book “A Probabilistic Theory of Pattern Recognition” by Luc Devroye, Laszlo Györfi, and Gabor Lugosi, the authors delve into the world of stochastic modelling and applied probability to explore how probabilistic methods can be used to improve pattern recognition algorithms.

    The book covers topics such as Bayesian decision theory, mixture models, and kernel methods, providing a comprehensive overview of the probabilistic techniques that underlie modern pattern recognition systems. By incorporating uncertainty into the pattern recognition process, probabilistic methods can better handle noisy data, adapt to changing environments, and provide more accurate predictions.

    Whether you are a researcher looking to deepen your understanding of pattern recognition or a practitioner seeking to improve the performance of your algorithms, “A Probabilistic Theory of Pattern Recognition” offers valuable insights into the power of stochastic modelling and applied probability in the field.
    #Probabilistic #Theory #Pattern #Recognition #Stochastic #Modelling #Applied #Probability

  • Probabilistic Machine Learning for Beginners: A Step-by-Step Quick Guide to Making Smarter Decisions with Data

    Probabilistic Machine Learning for Beginners: A Step-by-Step Quick Guide to Making Smarter Decisions with Data


    Price: $18.99
    (as of Dec 27,2024 09:19:16 UTC – Details)



    Are you interested in diving into the world of machine learning but feeling overwhelmed by the complexity of the topic? Look no further! In this quick guide, we will introduce you to the basics of probabilistic machine learning and show you how to start making smarter decisions with data.

    Step 1: Understanding the Basics

    Probabilistic machine learning is a branch of machine learning that deals with uncertainty and variability in data. Instead of providing a single prediction or classification, probabilistic models give you a range of possible outcomes along with their probabilities.

    Step 2: Choose the Right Model

    There are various probabilistic models you can use, such as Bayesian networks, Gaussian processes, and Markov chains. Choose the model that best fits your data and the problem you are trying to solve.

    Step 3: Data Preprocessing

    Before you can start training your model, you need to preprocess your data. This may include cleaning up missing values, scaling your features, and encoding categorical variables.

    Step 4: Training Your Model

    Once your data is ready, it’s time to train your probabilistic model. This involves feeding your data into the model and adjusting its parameters to minimize the error or maximize the likelihood of the data.

    Step 5: Making Predictions

    After training your model, you can use it to make predictions on new, unseen data. The model will provide you with a range of possible outcomes along with their probabilities, helping you make more informed decisions.

    In conclusion, probabilistic machine learning is a powerful tool that can help you make smarter decisions with data. By understanding the basics, choosing the right model, preprocessing your data, training your model, and making predictions, you can start harnessing the power of probabilistic machine learning in your own projects. Happy learning!
    #Probabilistic #Machine #Learning #Beginners #StepbyStep #Quick #Guide #Making #Smarter #Decisions #Data

  • Probabilistic Robotics (Intelligent Robotics and Autonomous Agents series)

    Probabilistic Robotics (Intelligent Robotics and Autonomous Agents series)


    Price: $62.99
    (as of Dec 27,2024 04:37:18 UTC – Details)




    ASIN ‏ : ‎ B08BT9H356
    Publisher ‏ : ‎ The MIT Press (August 19, 2005)
    Publication date ‏ : ‎ August 19, 2005
    Language ‏ : ‎ English
    File size ‏ : ‎ 47275 KB
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 858 pages


    Probabilistic Robotics: Understanding the World through Uncertainty

    In the field of robotics, understanding and navigating through uncertain environments is a crucial challenge. Probabilistic robotics offers a powerful framework for addressing this challenge, allowing robots to make decisions based on probabilistic reasoning and uncertainty.

    In the book “Probabilistic Robotics” from the Intelligent Robotics and Autonomous Agents series, authors Sebastian Thrun, Wolfram Burgard, and Dieter Fox provide a comprehensive introduction to the principles and techniques of probabilistic robotics. From basic concepts to advanced algorithms, this book covers everything you need to know to develop intelligent and autonomous robotic systems.

    With a focus on probabilistic state estimation, sensor fusion, and decision-making under uncertainty, “Probabilistic Robotics” equips readers with the tools to build robots that can navigate complex environments, interact with humans, and adapt to changing conditions.

    Whether you are a researcher, engineer, or student interested in robotics, this book is a valuable resource for understanding the fundamental principles of probabilistic robotics and applying them to real-world robotic systems. Dive into the world of uncertainty and probability with “Probabilistic Robotics” and take your robotics projects to the next level.
    #Probabilistic #Robotics #Intelligent #Robotics #Autonomous #Agents #series

  • Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)

    Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)


    Price: $84.99
    (as of Dec 27,2024 01:27:39 UTC – Details)



    Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)

    Probabilistic graphical models (PGMs) have gained significant attention in the field of artificial intelligence and machine learning due to their ability to model complex relationships between variables and make probabilistic inferences. In the book “Probabilistic Graphical Models: Principles and Applications,” authors explore the fundamental principles behind PGMs and their applications in computer vision and pattern recognition.

    The book covers topics such as Bayesian networks, Markov networks, and inference algorithms, providing a comprehensive understanding of the theoretical foundations of PGMs. Readers will learn how to use PGMs to model real-world problems, such as image recognition, object tracking, and natural language processing.

    With contributions from leading experts in the field, this book is a valuable resource for researchers, practitioners, and students interested in understanding the principles and applications of probabilistic graphical models in computer vision and pattern recognition. Whether you are new to the field or looking to deepen your knowledge, this book offers a comprehensive overview of PGMs and their practical applications.
    #Probabilistic #Graphical #Models #Principles #Applications #Advances #Computer #Vision #Pattern #Recognition

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

    Probabilistic Deep Learning: With Python, Keras and Tensorflow Probability



    Probabilistic Deep Learning: With Python, Keras and Tensorflow Probability

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    Probabilistic Deep Learning: With Python, Keras and Tensorflow Probability

    In the world of machine learning, probabilistic deep learning is a rapidly growing field that focuses on incorporating uncertainty into deep learning models. By treating model predictions as probability distributions rather than point estimates, probabilistic deep learning allows for more robust and reliable predictions.

    In this post, we will explore how to implement probabilistic deep learning using Python, Keras, and Tensorflow Probability. We will cover topics such as Bayesian neural networks, variational inference, and uncertainty quantification.

    By the end of this post, you will have a solid understanding of how to leverage probabilistic deep learning techniques in your own projects using popular libraries such as Keras and Tensorflow Probability. Let’s dive in!
    #Probabilistic #Deep #Learning #Python #Keras #Tensorflow #Probability

  • Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python

    Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python


    Price: $79.99 – $58.99
    (as of Dec 26,2024 19:04:03 UTC – Details)


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    Sharing the knowledge of experts

    O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.

    Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.

    Publisher ‏ : ‎ O’Reilly Media; 1st edition (September 19, 2023)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 264 pages
    ISBN-10 ‏ : ‎ 1492097675
    ISBN-13 ‏ : ‎ 978-1492097679
    Item Weight ‏ : ‎ 15.2 ounces
    Dimensions ‏ : ‎ 6.9 x 0.7 x 9.1 inches


    Probabilistic Machine Learning for Finance and Investing:
    A Primer to Generative AI with Python

    In the world of finance and investing, making accurate predictions and decisions is crucial for success. Probabilistic machine learning, a subset of artificial intelligence, offers a powerful tool for analyzing and predicting market behavior. By using probabilistic models, investors can better understand uncertain and complex market dynamics, leading to more informed investment decisions.

    One of the key components of probabilistic machine learning is generative AI, which focuses on creating realistic and diverse samples from a given dataset. This approach can be particularly useful in finance and investing, where predicting future market trends and behaviors is essential.

    Python, a popular programming language for data analysis and machine learning, is a great tool for implementing probabilistic machine learning models. In this primer, we will explore how to use Python to build generative AI models for financial data analysis and prediction.

    We will cover topics such as:
    – Introduction to probabilistic machine learning and generative AI
    – Building generative models using Python libraries such as TensorFlow and PyTorch
    – Analyzing financial data and creating predictive models
    – Evaluating model performance and making investment decisions based on model outputs

    Whether you are a seasoned investor looking to enhance your decision-making process or a data scientist interested in applying machine learning to finance, this primer will provide you with the tools and knowledge to leverage probabilistic machine learning for better financial outcomes. Stay tuned for more insights and tutorials on this exciting topic!
    #Probabilistic #Machine #Learning #Finance #Investing #Primer #Generative #Python,machine learning

  • Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning (Karlsruher Schriftenreihe Fahrzeugsystemtechnik)

    Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning (Karlsruher Schriftenreihe Fahrzeugsystemtechnik)


    Price: $69.00
    (as of Dec 24,2024 21:05:59 UTC – Details)




    Publisher ‏ : ‎ KIT Scientific Publishing (September 3, 2024)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 192 pages
    ISBN-10 ‏ : ‎ 3731513714
    ISBN-13 ‏ : ‎ 978-3731513711
    Item Weight ‏ : ‎ 13.1 ounces
    Dimensions ‏ : ‎ 5.83 x 0.44 x 8.27 inches


    In a new study published in the Karlsruher Schriftenreihe Fahrzeugsystemtechnik, researchers have developed a novel approach for predicting energy demand and driving range for electric vehicles using federated learning.

    Federated learning is a machine learning technique that allows multiple parties to collaborate on a shared model without sharing their raw data. This approach is particularly useful for predicting energy demand and driving range for electric vehicles, as it allows for the aggregation of data from various sources while maintaining privacy and security.

    The researchers used a probabilistic prediction model to estimate energy demand and driving range for electric vehicles based on factors such as weather conditions, traffic patterns, and driving behavior. By leveraging federated learning, the model was able to continuously learn and improve from data collected from different sources, leading to more accurate predictions.

    This study represents a significant advancement in the field of electric vehicle technology, as accurate predictions of energy demand and driving range are essential for optimizing vehicle performance and improving user experience. The researchers hope that their findings will help accelerate the adoption of electric vehicles and contribute to a more sustainable transportation system.
    #Probabilistic #Prediction #Energy #Demand #Driving #Range #Electric #Vehicles #Federated #Learning #Karlsruher #Schriftenreihe #Fahrzeugsystemtechnik

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