Ensemble Learning For Ai Developers
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Ensemble Learning For Ai Developers
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Author : Alok Kumar
language : en
Publisher: Apress
Release Date : 2020-06-19
Ensemble Learning For Ai Developers written by Alok Kumar and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-19 with Computers categories.
Use ensemble learning techniques and models to improve your machine learning results. Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices to guide you in combining models and using tools to boost performance of your machine learning projects. They teach you how to effectively implement ensemble concepts such as stacking and boosting and to utilize popular libraries such as Keras, Scikit Learn, TensorFlow, PyTorch, and Microsoft LightGBM. Tips are presented to apply ensemble learning in different data science problems, including time series data, imaging data, and NLP. Recent advances in ensemble learning are discussed. Sample code is provided in the form of scripts and the IPython notebook. What You Will Learn Understand the techniques and methods utilized in ensemble learning Use bagging, stacking, and boosting to improve performance of your machine learning projects by combining models to decrease variance, improve predictions, and reduce bias Enhance your machine learning architecture with ensemble learning Who This Book Is For Data scientists and machine learning engineers keen on exploring ensemble learning
Introduction To Generative Ai Second Edition
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Author : Numa Dhamani
language : en
Publisher: Simon and Schuster
Release Date : 2026-01-13
Introduction To Generative Ai Second Edition written by Numa Dhamani 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 2026-01-13 with Computers categories.
Get up to speed quickly with generative AI! AI tools like ChatGPT and Gemini, automated coding tools like Cursor and Copilot, and countless LLM-powered agents have become a part of daily life. They’ve also spawned a storm of misinformation, hype, and doomsaying that makes it tough to understand exactly what Generative AI actually is and what it can really do. Introduction to Generative AI, Second Edition delivers a clearly-written survey of generative AI fundamentals along with the techniques and strategies you need to use AI safely and effectively. In this easy-to-read introduction, you’ll learn: • How large language models (LLMs) work • How to apply AI across personal and professional work • The social, legal, and policy landscape around generative AI • Emerging trends like reasoning models and vibe coding Introduction to Generative AI, Second Edition guides you from your first eye-opening interaction with tools like ChatGPT to how AI tools can transform your personal and professional life safely and responsibly. This second edition has been completely revised to reflect the latest developments in the field—from the latest innovations in prompt engineering and AI agents to fresh coverage of multimodal training, reasoning models, no-code tools, retrieval-augmented generation (RAG), and more. About the book Introduction to Generative AI, Second Edition is an up-to-date guide to the capabilities, risks, and limitations of tools like ChatGPT, Gemini, and Claude. This easy-to-follow guide moves quickly through the basics, giving you the skills and understanding to use AI with confidence. Along the way, you’ll explore how AI is impacting even established industries, with an expert-level look at global investment in AI, AI education policy, AI’s economic impact, and the ongoing legal and ethical issues of AI usage. About the reader For anyone interested in generative AI. No technical experience required. About the author Numa Dhamani is a natural language processing expert working at the intersection of technology and society. Maggie Engler is an engineer and researcher currently working on safety for large language models.
Decision Making In Business Management Through Artificial Intelligence And Machine Learning
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Author : Joeleen Kimbell
language : en
Publisher: GRIN Verlag
Release Date : 2025-02-03
Decision Making In Business Management Through Artificial Intelligence And Machine Learning written by Joeleen Kimbell and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-03 with Business & Economics categories.
Case Study from the year 2024 in the subject Business economics - E-Commerce, grade: A, , language: English, abstract: This paper focuses on how Artificial Intelligence and Machine Learning have changed decisions in retailing, healthcare, financing, and manufacturing careers. They demonstrate how AI is used in supply chain management to support the decision-making process by making forecasts, processing data, and optimizing operations, leading to higher efficiency, decreased costs, and increased customer satisfaction. Thus, the research incorporates quantitative and qualitative approaches, such as surveys and interviews with key stakeholders, and employs statistical and content analysis methods While adopting the decision theory and systems thinking perspectives, this research paper highlights the necessity of effectively and adequately implementing AI into an organization permanently to achieve more benefits. The following are realizable out-of-the-box solutions that the study suggests, including audiences for employees, data protection for compliance, and conscientization of fairness in AI algorithms. Future directions include situations where these applications are to be broadened to weigh on ethical issues and to encourage optimal technological fairness that will, in turn, ensure sustainable business improvement and innovation.
Official Google Cloud Certified Associate Cloud Engineer Study Guide
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Author : Dan Sullivan
language : en
Publisher: John Wiley & Sons
Release Date : 2019-04-16
Official Google Cloud Certified Associate Cloud Engineer Study Guide written by Dan Sullivan and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-16 with Computers categories.
The Only Official Google Cloud Study Guide The Official Google Cloud Certified Associate Cloud Engineer Study Guide, provides everything you need to prepare for this important exam and master the skills necessary to land that coveted Google Cloud Engineering certification. Beginning with a pre-book assessment quiz to evaluate what you know before you begin, each chapter features exam objectives and review questions, plus the online learning environment includes additional complete practice tests. Written by Dan Sullivan, a popular and experienced online course author for machine learning, big data, and Cloud topics, Official Google Cloud Certified Associate Cloud Engineer Study Guide is your ace in the hole for deploying and managing Google Cloud Services. Select the right Google service from the various choices based on the application to be built Compute with Cloud VMs and managing VMs Plan and deploying storage Network and configure access and security Google Cloud Platform is a leading public cloud that provides its users to many of the same software, hardware, and networking infrastructure used to power Google services. Businesses, organizations, and individuals can launch servers in minutes, store petabytes of data, and implement global virtual clouds with the Google Cloud Platform. Certified Associate Cloud Engineers have demonstrated the knowledge and skills needed to deploy and operate infrastructure, services, and networks in the Google Cloud. This exam guide is designed to help you understand the Google Cloud Platform in depth so that you can meet the needs of those operating resources in the Google Cloud.
Artificial Intelligence In Engineering Design Models Of Innovative Design Reasoning About Physical Systems And Reasoning About Geometry
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Author : Christopher Tong
language : en
Publisher:
Release Date : 1992
Artificial Intelligence In Engineering Design Models Of Innovative Design Reasoning About Physical Systems And Reasoning About Geometry written by Christopher Tong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Artificial intelligence categories.
V.1. Design representation and models of routine design. v.2. Models of innovat ive design, reasoning about physical systems, and reasoning about geometry. v.3 . Knowledge acquisition, commercial systems, and integrated environments.
Artificial Intelligence In Engineering Design Design Representation And Models Of Routine Design
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Author : Christopher Tong
language : en
Publisher:
Release Date : 1992
Artificial Intelligence In Engineering Design Design Representation And Models Of Routine Design written by Christopher Tong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Artificial intelligence categories.
Artificial Intelligence In Engineering Design Knowledge Acquisition Commercial Systems And Integrated Environments
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Author : D. Sriram
language : en
Publisher:
Release Date : 1992
Artificial Intelligence In Engineering Design Knowledge Acquisition Commercial Systems And Integrated Environments written by D. Sriram and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Artificial intelligence categories.
Artificial Intelligence In Engineering Design
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Author : Christopher Tong
language : en
Publisher: Academic Press
Release Date : 1992-10-21
Artificial Intelligence In Engineering Design written by Christopher Tong and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992-10-21 with Computers categories.
V.1. Design representation and models of routine design. v.2. Models of innovat ive design, reasoning about physical systems, and reasoning about geometry. v.3 . Knowledge acquisition, commercial systems, and integrated environments.
Knowledge Engineering
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Author : G. Steven Tuthill
language : en
Publisher: T A B/T P R
Release Date : 1990
Knowledge Engineering written by G. Steven Tuthill and has been published by T A B/T P R this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Computers categories.
Machine Learning And Deep Learning Using Python And Tensorflow
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Author : Venkata Reddy Konasani
language : en
Publisher: McGraw Hill Professional
Release Date : 2021-04-29
Machine Learning And Deep Learning Using Python And Tensorflow written by Venkata Reddy Konasani and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-29 with Technology & Engineering categories.
Understand the principles and practices of machine learning and deep learning This hands-on guide lays out machine learning and deep learning techniques and technologies in a style that is approachable, using just the basic math required. Written by a pair of experts in the field, Machine Learning and Deep Learning Using Python and TensorFlow contains case studies in several industries, including banking, insurance, e-commerce, retail, and healthcare. The book shows how to utilize machine learning and deep learning functions in today’s smart devices and apps. You will get download links for datasets, code, and sample projects referred to in the text. Coverage includes: Machine learning and deep learning concepts Python programming and statistics fundamentals Regression and logistic regression Decision trees Model selection and cross-validation Cluster analysis Random forests and boosting Artificial neural networks TensorFlow and Keras Deep learning hyperparameters Convolutional neural networks Recurrent neural networks and long short-term memory