Lightgbm In Practice
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Lightgbm In Practice
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-11
Lightgbm In Practice written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-11 with Computers categories.
"LightGBM in Practice" "LightGBM in Practice" offers a comprehensive and authoritative exploration of one of the most powerful tools in the modern machine learning landscape. Beginning with in-depth coverage of LightGBM's foundational principles—such as gradient boosting decision trees, histogram-based learning, and innovative strategies for scalable performance—this book demystifies the underlying algorithms that fuel LightGBM’s speed and accuracy. Through clear explanations and mathematical rigor, readers will gain a deep understanding of both the theoretical and practical underpinnings that set LightGBM apart, including exclusive feature bundling, gradient-based sampling, and scalable system architecture. As the journey continues, "LightGBM in Practice" seamlessly bridges theory with real-world engineering. Readers will learn sophisticated data preparation and feature engineering techniques tailored for large-scale tabular and sparse datasets, discover best practices for distributed and GPU-accelerated training, and master advanced model optimization, hyperparameter tuning, and integration within enterprise ML pipelines. Dedicated chapters address model interpretability with industry-leading tools like SHAP and LIME, while also covering the nuances of regulatory compliance, auditability, and transparency—making the book indispensable for production-grade deployment in mission-critical environments. The final sections delve into specialized applications, operational strategies, and the future of LightGBM. From time series forecasting and recommendation engines to privacy preservation and fairness audits, "LightGBM in Practice" empowers practitioners to securely and robustly deploy, monitor, and scale models across diverse domains—including finance, health, and regulated industries. Complete with case studies, actionable code insights, and guidance for contributing to the open-source ecosystem, this essential guide ensures readers remain at the forefront of gradient boosting innovations for years to come.
Ensemble Methods For Machine Learning
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Author : Gautam Kunapuli
language : en
Publisher: Simon and Schuster
Release Date : 2023-05-02
Ensemble Methods For Machine Learning written by Gautam Kunapuli and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-02 with Computers categories.
Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real-world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models.
Next Level Data Science
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2024-11-04
Next Level Data Science written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-04 with Computers categories.
Data science is a relatively new term coined in the past decade. While it shares much in common with traditional statistics, it warrants its own name, as modern computer technology has introduced tools that can tackle challenges previously unsolvable, such as machine learning models. However, these new tools demand new techniques. You might be surprised to find that even slight adjustments to hyperparameters or changes in data preprocessing can significantly alter a model’s output. This ebook concentrates on two fundamental yet widely applicable models in data science: linear regression and decision trees. The focus here isn’t just to explain these models but to use them as examples, illustrating the key considerations you should bear in mind when working on a data science project. Next Level Data Science is designed to help you cultivate an effective mindset for data science projects, enabling you to work more efficiently. Written in the approachable and engaging style you know from Machine Learning Mastery, this ebook will guide you on where to start and what to prioritize when drawing insights from data.
Computer Science And Education
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Author : Wenxing Hong
language : en
Publisher: Springer Nature
Release Date : 2023-05-13
Computer Science And Education written by Wenxing Hong and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-13 with Computers categories.
This three-volume set constitues selected papers presented during the 17th International Conference on Computer Science and Education, ICCSE 2022, held in Ningbo, China, in August 2022. The 168 full papers and 43 short papers presented were thoroughly reviewed and selected from the 510 submissions. They focus on a wide range of computer science topics, especially AI, data science, and engineering, and technology-based education, by addressing frontier technical and business issues essential to the applications of data science in both higher education and advancing e-Society.
Iot And Ai Enabled Healthcare Solutions And Intelligent Disease Prediction
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Author : Bhoopesh Singh Bhati
language : en
Publisher: CRC Press
Release Date : 2025-09-09
Iot And Ai Enabled Healthcare Solutions And Intelligent Disease Prediction written by Bhoopesh Singh Bhati and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-09 with Computers categories.
The book presents fundamental to advanced concepts of AI and IoT in healthcare and disease prediction, demonstrating the emerging mechanisms, including machine learning, deep learning, image sensing, and explainable AI models to handle issues in healthcare industries with real-life scenarios. Included chapters are contributed by experienced professionals and academicians who examine severe diseases, applications, models, tools, frameworks, case studies, applications, and best practices in Healthcare. This book integrates the medical domain with AI technology. It covers trending explainable AI, computer vision (CV), and IoT that facilitate automation for healthcare solutions and medical diagnostics. The primary focus on explainable AI uncovers the black box of deep learning and bridges the distance between medical professionals and technologists. IoT in Healthcare: provides a mechanism of image sensing and is helpful in surgical tools.
Mastering Azure Machine Learning
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Author : Christoph Körner
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-04-30
Mastering Azure Machine Learning written by Christoph Körner and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-30 with Computers categories.
Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes Key FeaturesMake sense of data on the cloud by implementing advanced analyticsTrain and optimize advanced deep learning models efficiently on Spark using Azure DatabricksDeploy machine learning models for batch and real-time scoring with Azure Kubernetes Service (AKS)Book Description The increase being seen in data volume today requires distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights and train and deploy machine learning (ML) models. This book will help you improve your knowledge of building ML models using Azure and end-to-end ML pipelines on the cloud. The book starts with an overview of an end-to-end ML project and a guide on how to choose the right Azure service for different ML tasks. It then focuses on Azure Machine Learning and takes you through the process of data experimentation, data preparation, and feature engineering using Azure Machine Learning and Python. You'll learn advanced feature extraction techniques using natural language processing (NLP), classical ML techniques, and the secrets of both a great recommendation engine and a performant computer vision model using deep learning methods. You'll also explore how to train, optimize, and tune models using Azure Automated Machine Learning and HyperDrive, and perform distributed training on Azure. Then, you'll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure Machine Learning, along with the basics of MLOps—DevOps for ML to automate your ML process as CI/CD pipeline. By the end of this book, you'll have mastered Azure Machine Learning and be able to confidently design, build and operate scalable ML pipelines in Azure. What you will learnSetup your Azure Machine Learning workspace for data experimentation and visualizationPerform ETL, data preparation, and feature extraction using Azure best practicesImplement advanced feature extraction using NLP and word embeddingsTrain gradient boosted tree-ensembles, recommendation engines and deep neural networks on Azure Machine LearningUse hyperparameter tuning and Azure Automated Machine Learning to optimize your ML modelsEmploy distributed ML on GPU clusters using Horovod in Azure Machine LearningDeploy, operate and manage your ML models at scaleAutomated your end-to-end ML process as CI/CD pipelines for MLOpsWho this book is for This machine learning book is for data professionals, data analysts, data engineers, data scientists, or machine learning developers who want to master scalable cloud-based machine learning architectures in Azure. This book will help you use advanced Azure services to build intelligent machine learning applications. A basic understanding of Python and working knowledge of machine learning are mandatory.
Ensemble Learning Algorithms With Python
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2021-04-26
Ensemble Learning Algorithms With Python written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-26 with Computers categories.
Predictive performance is the most important concern on many classification and regression problems. Ensemble learning algorithms combine the predictions from multiple models and are designed to perform better than any contributing ensemble member. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and effectively improve predictive modeling performance using ensemble algorithms.
Application Of Machine Learning In Slope Stability Assessment
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Author : Zhang Wengang
language : en
Publisher: Springer Nature
Release Date : 2023-07-08
Application Of Machine Learning In Slope Stability Assessment written by Zhang Wengang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-08 with Technology & Engineering categories.
This book focuses on the application of machine learning in slope stability assessment. The contents include: overview of machine learning approaches, the mainstream smart in-situ monitoring techniques, the applications of the main machine learning algorithms, including the supervised learning, unsupervised learning, semi- supervised learning, reinforcement learning, deep learning, ensemble learning, etc., in slope engineering and landslide prevention, introduction of the smart in-situ monitoring and slope stability assessment based on two well-documented case histories, the prediction of slope stability using ensemble learning techniques, the application of Long Short-Term Memory Neural Network and Prophet Algorithm in Slope Displacement Prediction, displacement prediction of Jiuxianping landslide using gated recurrent unit (GRU) networks, seismic stability analysis of slopes subjected to water level changes using gradient boosting algorithms, efficient reliability analysis of slopes in spatially variable soils using XGBoost, efficient time-variant reliability analysis of Bazimen landslide in the Three Gorges Reservoir Area using XGBoost and LightGBM algorithms, as well as the future work recommendation.The authors also provided their own thoughts learnt from these applications as well as work ongoing and future recommendations.
Advanced Forecasting With Python
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Author : Joos Korstanje
language : en
Publisher: Apress
Release Date : 2021-07-03
Advanced Forecasting With Python written by Joos Korstanje and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-03 with Computers categories.
Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook’s open-source Prophet model, and Amazon’s DeepAR model. Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models followed by advanced machine learning and deep learning models. It concludes with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models. Each of the models presented in this book is covered in depth, with an intuitive simple explanation of the model, a mathematical transcription of the idea, and Python code that applies the model to an example data set. Reading this book will add a competitive edge to your current forecasting skillset. The book is also adapted to those who have recently started working on forecasting tasks and are looking for an exhaustive book that allows them to start with traditional models and gradually move into more and more advanced models. What You Will Learn Carry out forecasting with Python Mathematically and intuitively understand traditional forecasting models and state-of-the-art machine learning techniques Gain the basics of forecasting and machine learning, including evaluation of models, cross-validation, and back testing Select the right model for the right use case Who This Book Is For The advanced nature of the later chapters makes the book relevant for applied experts working in the domain of forecasting, as the models covered have been published only recently. Experts working in the domain will want to update their skills as traditional models are regularly being outperformed by newer models.
Integrating Artificial Intelligence In Cybersecurity And Forensic Practices
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Author : Omar, Marwan
language : en
Publisher: IGI Global
Release Date : 2024-12-06
Integrating Artificial Intelligence In Cybersecurity And Forensic Practices written by Omar, Marwan and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-06 with Computers categories.
The exponential rise in digital transformation has brought unprecedented advances and complexities in cybersecurity and forensic practices. As cyber threats become increasingly sophisticated, traditional security measures alone are no longer sufficient to counter the dynamic landscape of cyber-attacks, data breaches, and digital fraud. The emergence of Artificial Intelligence (AI) has introduced powerful tools to enhance detection, response, and prevention capabilities in cybersecurity, providing a proactive approach to identifying potential threats and securing digital environments. In parallel, AI is transforming digital forensic practices by automating evidence collection, enhancing data analysis accuracy, and enabling faster incident response times. From anomaly detection and pattern recognition to predictive modeling, AI applications in cybersecurity and forensics hold immense promise for creating robust, adaptive defenses and ensuring timely investigation of cyber incidents. Integrating Artificial Intelligence in Cybersecurity and Forensic Practices explores the evolving role of AI in cybersecurity and forensic science. It delves into key AI techniques, discussing their applications, benefits, and challenges in tackling modern cyber threats and forensic investigations. Covering topics such as automation, deep neural networks, and traffic analysis, this book is an excellent resource for professionals, researchers, students, IT security managers, threat analysts, digital forensic investigators, and more.