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Tag: Explainable

  • 3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods

    3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods


    Price: $129.99 – $100.71
    (as of Dec 15,2024 09:16:18 UTC – Details)




    Publisher ‏ : ‎ Springer; 1st ed. 2021 edition (December 11, 2021)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 160 pages
    ISBN-10 ‏ : ‎ 3030891798
    ISBN-13 ‏ : ‎ 978-3030891794
    Item Weight ‏ : ‎ 14.2 ounces
    Dimensions ‏ : ‎ 6.14 x 0.44 x 9.21 inches


    Point cloud analysis is a crucial aspect of various industries, including robotics, autonomous vehicles, and urban planning. Traditional methods have been widely used for analyzing point cloud data, but with the advancements in technology, deep learning and explainable machine learning methods are gaining popularity. In this post, we will discuss these three approaches and compare their strengths and weaknesses.

    Traditional Methods:
    Traditional methods for point cloud analysis typically involve algorithms such as clustering, segmentation, and feature extraction. These methods are often based on mathematical models and require manual tuning of parameters. While they have been successful in many applications, traditional methods may struggle with complex and noisy point cloud data.

    Deep Learning:
    Deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown promising results in point cloud analysis. These methods can automatically learn features from the data and are able to handle large and complex datasets. However, deep learning models typically require a large amount of labeled data for training, which can be a bottleneck in some applications.

    Explainable Machine Learning:
    Explainable machine learning methods, such as decision trees and rule-based models, aim to provide transparency and interpretability in the analysis process. These methods can help users understand the reasoning behind the model’s predictions, which is crucial in safety-critical applications. While explainable machine learning methods may not always achieve the same level of accuracy as deep learning models, they offer more insights into the decision-making process.

    In conclusion, each of these approaches has its own strengths and weaknesses in point cloud analysis. Traditional methods are reliable and well-established, while deep learning methods offer high accuracy but require large amounts of data. Explainable machine learning methods provide transparency and interpretability, which are essential in many real-world applications. It is important to choose the right approach based on the specific requirements of the task at hand.
    #Point #Cloud #Analysis #Traditional #Deep #Learning #Explainable #Machine #Learning #Methods

  • Interpretable Machine Learning with Python: Build explainable, fair, and robust high-performance models with hands-on, real-world examples

    Interpretable Machine Learning with Python: Build explainable, fair, and robust high-performance models with hands-on, real-world examples


    Price: $49.99 – $35.56
    (as of Dec 04,2024 17:21:15 UTC – Details)


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    Publisher ‏ : ‎ Packt Publishing; 2nd ed. edition (October 31, 2023)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 606 pages
    ISBN-10 ‏ : ‎ 180323542X
    ISBN-13 ‏ : ‎ 978-1803235424
    Item Weight ‏ : ‎ 2.29 pounds
    Dimensions ‏ : ‎ 1.36 x 7.5 x 9.25 inches


    Interpretable Machine Learning with Python: Build explainable, fair, and robust high-performance models with hands-on, real-world examples

    In the world of machine learning, building models that are not only accurate but also interpretable, fair, and robust is crucial. With the increasing reliance on AI and ML systems in various industries, the need for transparency and accountability in these models has never been higher.

    In this post, we will explore how to achieve these goals using Python, a popular programming language for data science and machine learning. We will cover techniques and tools that can help you build models that are not only high-performing but also easy to understand, fair, and resistant to various types of biases.

    Some of the topics we will cover include:

    – Interpretable machine learning techniques such as feature importance analysis, partial dependence plots, and local interpretable model-agnostic explanations (lime)
    – Fairness in machine learning, including strategies for mitigating biases and ensuring equal treatment for all individuals
    – Robustness in machine learning, including techniques for detecting and handling adversarial attacks and other forms of model manipulation

    Throughout the post, we will provide hands-on examples and real-world case studies to illustrate these concepts in action. By the end of this post, you will have a solid understanding of how to build interpretable, fair, and robust machine learning models using Python.
    #Interpretable #Machine #Learning #Python #Build #explainable #fair #robust #highperformance #models #handson #realworld #examples

  • Machine Learning for Engineers: Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications

    Machine Learning for Engineers: Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications


    Price: $54.99
    (as of Nov 29,2024 07:49:44 UTC – Details)




    Publisher ‏ : ‎ Springer; 2024th edition (December 31, 2024)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 258 pages
    ISBN-10 ‏ : ‎ 366269994X
    ISBN-13 ‏ : ‎ 978-3662699942
    Item Weight ‏ : ‎ 1.11 pounds


    Machine Learning for Engineers: Introduction to Physics-Informed, Explainable Learning Methods for AI in Engineering Applications

    In recent years, machine learning has become an indispensable tool for engineers in various fields, including aerospace, automotive, civil, and mechanical engineering. With the advancement of AI technologies, engineers are now able to leverage machine learning algorithms to solve complex engineering problems more efficiently and accurately.

    One of the emerging trends in machine learning for engineering applications is the use of physics-informed and explainable learning methods. These methods combine the power of traditional machine learning algorithms with domain-specific knowledge of physics and engineering principles to improve the performance and interpretability of AI models.

    Physics-informed learning methods aim to incorporate known physical laws and constraints into the machine learning models, making them more robust and reliable in predicting real-world phenomena. By integrating physics-based constraints into the training process, engineers can ensure that the AI models are not only accurate but also physically meaningful and interpretable.

    Explainable learning methods, on the other hand, focus on providing insights into how the AI models make predictions and decisions. These methods aim to enhance the transparency and trustworthiness of machine learning models by enabling engineers to understand the underlying logic and reasoning behind the predictions. This is crucial in engineering applications where the stakes are high, and decisions based on AI models can have significant implications.

    Overall, physics-informed and explainable learning methods offer a promising approach to harness the power of AI in engineering applications while ensuring the reliability, interpretability, and trustworthiness of the models. As engineers continue to explore the potential of machine learning in their field, incorporating these advanced techniques will be essential for pushing the boundaries of what is possible and driving innovation in engineering.
    #Machine #Learning #Engineers #Introduction #PhysicsInformed #Explainable #Learning #Methods #Engineering #Applications

  • Big Data Analytics Framework for Smart Grids (Explainable AI (XAI) for Engineering Applications)

    Big Data Analytics Framework for Smart Grids (Explainable AI (XAI) for Engineering Applications)


    Price: $160.00 – $133.59
    (as of Nov 27,2024 12:28:14 UTC – Details)




    Publisher ‏ : ‎ CRC Press; 1st edition (December 22, 2023)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 232 pages
    ISBN-10 ‏ : ‎ 1032392908
    ISBN-13 ‏ : ‎ 978-1032392905
    Item Weight ‏ : ‎ 15.8 ounces
    Dimensions ‏ : ‎ 0.58 x 6.14 x 9.21 inches


    In the age of smart grids, the integration of big data analytics is essential for optimizing energy management and ensuring the efficiency of power distribution systems. One of the key components of this framework is Explainable AI (XAI), which plays a crucial role in making complex engineering applications more transparent and understandable.

    Explainable AI is the ability of AI systems to provide clear explanations for their decisions and predictions, allowing engineers and operators to better understand how these systems work and how they can be improved. In the context of smart grids, XAI can help identify potential issues in the power distribution network, optimize energy consumption, and enhance the overall performance of the system.

    By integrating XAI into big data analytics frameworks for smart grids, engineers can gain valuable insights into the inner workings of the power distribution system, enabling them to make more informed decisions and take proactive measures to prevent potential failures or disruptions. XAI can also help improve the accuracy and reliability of predictive models, leading to more efficient energy management and reduced operational costs.

    Overall, the combination of big data analytics and Explainable AI represents a powerful tool for enhancing the efficiency and reliability of smart grids. By leveraging the insights provided by XAI, engineers can optimize energy management, improve system performance, and ensure the seamless operation of power distribution networks in the era of smart grids.
    #Big #Data #Analytics #Framework #Smart #Grids #Explainable #XAI #Engineering #Applications