Gradient Boosting In Automatic Machine Learning Feature Selection And Hyperparameter Optimization
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Gradient Boosting In Automatic Machine Learning Feature Selection And Hyperparameter Optimization
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Author : Janek Thomas
language : en
Publisher:
Release Date : 2019
Gradient Boosting In Automatic Machine Learning Feature Selection And Hyperparameter Optimization written by Janek Thomas and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
The Neurips 18 Competition
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Author : Sergio Escalera
language : en
Publisher: Springer Nature
Release Date : 2019-11-29
The Neurips 18 Competition written by Sergio Escalera and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-29 with Computers categories.
This volume presents the results of the Neural Information Processing Systems Competition track at the 2018 NeurIPS conference. The competition follows the same format as the 2017 competition track for NIPS. Out of 21 submitted proposals, eight competition proposals were selected, spanning the area of Robotics, Health, Computer Vision, Natural Language Processing, Systems and Physics. Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they assess the practicability of an algorithmic solution in addition to assessing feasibility. The eight run competitions aim at advancing the state of the art in deep reinforcement learning, adversarial learning, and auto machine learning, among others, including new applications for intelligent agents in gaming and conversational settings, energy physics, and prosthetics.
Feature Engineering For Modern Machine Learning With Scikit Learn
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Author : Cuantum Technologies LLC
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-01-23
Feature Engineering For Modern Machine Learning With Scikit Learn written by Cuantum Technologies LLC 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 2025-01-23 with Computers categories.
Master feature engineering with Scikit-Learn! Learn to preprocess, transform, and automate data for machine learning. Boost predictive accuracy with pipelines, clustering, and advanced techniques for real-world projects. Key Features Comprehensive guide to feature engineering for Scikit-Learn Hands-on projects for real-world applications Focus on automation, pipelines, and deep learning integration Book DescriptionFeature engineering is essential for building robust predictive models. This book delves into practical techniques for transforming raw data into powerful features using Scikit-Learn. You'll explore automation, deep learning integrations, and advanced topics like feature selection and model evaluation. Learn to handle real-world data challenges, enhance accuracy, and streamline your workflows. Through hands-on projects, readers will gain practical experience with techniques such as clustering, pipelines, and feature selection, applied to domains like retail and healthcare. Step-by-step instructions ensure a comprehensive learning journey, from foundational concepts to advanced automation and hybrid modeling approaches. By combining theory with real-world applications, the book equips data professionals with the tools to unlock the full potential of machine learning models. Whether working with structured datasets or integrating deep learning features, this guide provides actionable insights to tackle any data transformation challenge effectively.What you will learn Create data-driven features for better ML models Apply Scikit-Learn pipelines for automation Use clustering and feature selection effectively Handle imbalanced datasets with advanced techniques Leverage regularization for feature selection Utilize deep learning for feature extraction Who this book is for Data scientists, machine learning engineers, and analytics professionals looking to improve predictive model performance will find this book invaluable. Prior experience with Python and basic machine learning concepts is recommended. Familiarity with Scikit-Learn is helpful but not required.
Applied Natural Language Processing In The Enterprise
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Author : Ankur A. Patel
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-05-12
Applied Natural Language Processing In The Enterprise written by Ankur A. Patel and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-12 with Computers categories.
NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With a basic understanding of machine learning and some Python experience, you'll learn how to build, train, and deploy models for real-world applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP. Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension Train NLP models with performance comparable or superior to that of out-of-the-box systems Learn about Transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai Build core parts of the NLP pipeline--including tokenizers, embeddings, and language models--from scratch using Python and PyTorch Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in production
Automated Machine Learning
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Author : Adnan Masood
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-02-18
Automated Machine Learning written by Adnan Masood 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 2021-02-18 with Computers categories.
Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies Key FeaturesGet up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choiceEliminate mundane tasks in data engineering and reduce human errors in machine learning modelsFind out how you can make machine learning accessible for all users to promote decentralized processesBook Description Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort. This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you’ll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle. By the end of this machine learning book, you’ll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks. What you will learnExplore AutoML fundamentals, underlying methods, and techniquesAssess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenarioFind out the difference between cloud and operations support systems (OSS)Implement AutoML in enterprise cloud to deploy ML models and pipelinesBuild explainable AutoML pipelines with transparencyUnderstand automated feature engineering and time series forecastingAutomate data science modeling tasks to implement ML solutions easily and focus on more complex problemsWho this book is for Citizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open source tools, Microsoft Azure Machine Learning, AWS, and Google Cloud Platform will find this book useful. Beginner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial.
Proceedings Of The 3rd International Conference On Machine Learning Cloud Computing And Intelligent Mining Mlccim2024
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Author : Fuchun Sun
language : en
Publisher: Springer Nature
Release Date : 2025-03-21
Proceedings Of The 3rd International Conference On Machine Learning Cloud Computing And Intelligent Mining Mlccim2024 written by Fuchun Sun and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-21 with Technology & Engineering categories.
This proceedings book offers a meticulously curated compilation of peer-reviewed papers presented at the 3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM2024). With a profound focus on these domains, this book serves as an invaluable resource for researchers, experts, professionals, and practitioners engaged in machine learning, control systems, robot, cloud computing, and intelligent mining techniques. The book facilitates a vibrant exchange of knowledge, enabling participants to unveil their pioneering research findings, showcase the outcomes of their latest projects, and engage in thought-provoking discussions to share perspectives and experiences.
Transformative Applied Research In Computing Engineering Science And Technology
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Author : Damayanthi Dahanayake
language : en
Publisher: CRC Press
Release Date : 2025-04-29
Transformative Applied Research In Computing Engineering Science And Technology written by Damayanthi Dahanayake and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-29 with Computers categories.
This was the first international conference conducted by NSBM Green University in Sri Lanka under the theme, “Breaking boundaries: pioneering solutions for global challenges”. It focused on a diverse community of scholars, researchers and practitioners from around the globe to explore innovative approaches and breakthroughs in applied research across various disciplines, i.e., computing, engineering, science and technology. It dived into engaging discussions, presentations, and workshops covering a wide array of transformative topics, spanning from cutting-edge advancements in technology and science to impactful solutions addressing pressing societal challenges. It provided a pivotal opportunity for both seasoned experts and budding researchers to convene, fostering the exchange of vital information, cutting-edge research ideas or technology and innovative ideas, forge collaborations and shape the future of applied research.
Catboost Algorithms And Applications
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-03
Catboost Algorithms And Applications 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 2025-06-03 with Computers categories.
"CatBoost Algorithms and Applications" "CatBoost Algorithms and Applications" offers a comprehensive and rigorous exploration of one of the most advanced gradient boosting frameworks in modern machine learning. The book begins with a deep dive into the mathematical foundations of CatBoost, dissecting key techniques such as ordered boosting, sophisticated handling of categorical variables, robust overfitting prevention, and the formal structure of symmetric trees. It unpacks CatBoost's internal mechanics, guiding the reader through the algorithm’s entire processing pipeline, memory and GPU optimizations, permutation policies, and extensibility for custom objectives — equipping practitioners with both theoretical mastery and practical insight. Building on these foundations, the book delves into advanced topics critical for real-world applications, including feature engineering, multimodal data integration, hyperparameter optimization, and automated machine learning workflows. Special emphasis is placed on model interpretability, fairness, and explainability, with dedicated chapters on SHAP values, bias assessment, model debugging, and governance—all vital for deploying responsible AI solutions. Readers will also learn to harness CatBoost at scale, with detailed architectures for distributed training, cloud deployment, resource management, and resilient production systems that support low-latency, high-throughput inference. Enriched with practical case studies, best practices, and guidance for emerging domains like time series forecasting and text data, "CatBoost Algorithms and Applications" culminates in an analysis of the latest research, current challenges, and the future trajectory of CatBoost in federated, privacy-preserving, and responsible machine learning. Designed for data scientists, engineers, and researchers, this book serves as both a definitive technical reference and a strategic resource for leveraging CatBoost to solve complex, enterprise-scale machine learning problems.
Practical Guide To H2o Ai
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-05-31
Practical Guide To H2o Ai 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 2025-05-31 with Computers categories.
"Practical Guide to H2O.ai" "Practical Guide to H2O.ai" is a comprehensive resource designed for data scientists, machine learning engineers, and IT professionals who seek to master the full capabilities of H2O.ai’s powerful platform. This guide delivers a deep dive into the architecture and components of the H2O ecosystem—including H2O-3 and Driverless AI—while demystifying its integration within diverse enterprise environments, whether on-premises, cloud, or hybrid. Readers will gain actionable insights into secure system deployment, cluster management, large-scale data ingestion, and optimized ETL workflows, ensuring robust infrastructure that meets the demands of modern data-driven organizations. Structured to support both practical adoption and technical excellence, the book traverses core machine learning tasks, from advanced preprocessing and feature engineering to supervised and unsupervised learning with leading algorithms such as GBM, XGBoost, and deep neural networks. Special emphasis is placed on scalable automation through H2O AutoML, presenting real-world case studies while showcasing best practices in algorithm selection, hyperparameter optimization, and model evaluation. Dedicated chapters explore explainable AI and responsible ML practices—covering interpretability, bias mitigation, compliance, and data privacy—empowering readers to build transparent, auditable, and trustworthy solutions for complex, regulated domains. With detailed coverage of emerging fields like natural language processing, time series analysis, MLOps, and distributed deep learning, "Practical Guide to H2O.ai" is an indispensable reference for leveraging H2O.ai at scale. Topics such as advanced model deployment, real-time inference, CI/CD integration, and production troubleshooting combine theory with hands-on strategies for operationalizing machine learning workflows. Whether you are scaling to petabyte data, orchestrating containerized clusters, or exploring cutting-edge areas like federated learning and edge ML, this guide equips you with the knowledge and tools to drive innovation and achieve enterprise-level AI success.
Computational Science And Its Applications Iccsa 2024
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Author : Osvaldo Gervasi
language : en
Publisher: Springer Nature
Release Date : 2024-07-01
Computational Science And Its Applications Iccsa 2024 written by Osvaldo Gervasi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-01 with Computers categories.
The two-volume LNCS set 14813 and 14814 constitutes the refereed proceedings of the 24th International Conference on Computational Science and Its Applications, ICCSA 2024, held in Hanoi, Vietnam, during July 1–4, 2024. The 53 full papers, 6 short papers and 3 PHD showcase papers included in these volumes were carefully reviewed and selected from a total of 207 submissions. The papers focus on the following six sub-areas within Computer Science and its Applications: Computational Methods, Algorithms and Scientific Applications; High Performance Computing and Networks; Geometric Modeling, Graphics and Visualization; Advanced and Emerging Applications; Information Systems and Technologies & Urban and Regional Planning.