SUPERVISED LEARNING TECHNIQUES: REGRESSION AND DYNAMIC MODELS. EXAMPLES WITH EVIEWS PDF Download

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Publisher: CESAR PEREZ
ISBN: 1716260116
Category : Computers
Languages : en
Pages : 234

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Book Description
Microsoft SQL Server is a relational database management system, developed by the company Microsoft. The development language used (by command line or through the Management Studio graphic interface) is Transact-SQL (TSQL), an implementation of the ANSI standard of the SQL language, used to manipulate and retrieve data (DML), create tables and define relationships between them (DDL). This book develops the design, management and administration of databases through the relational language TRANSACT SQL

Author: PEREZ. C. PEREZ
Publisher:
ISBN: 9781716260223
Category :
Languages : en
Pages : 0

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Author: C Perez
Publisher: Independently Published
ISBN:
Category :
Languages : en
Pages : 234

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Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outcomes, and unsupervised learning, which finds hidden patterns or intrinsic structures in the input data. In this book, supervised learning techniques (predictive techniques) related to regression will be developed. More specifically, we will go deeper into the linear models multiple regression with all their problems of identification, estimation and diagnosis. Dynamic models and univariate time series models are also contemplated. Almon, Koyck, Klein, and other dynamic models are developed. An important part of the content is the structural changes and stability in dynamic predictive models, unit roots and co-integration. A great variety of examples and practical exercises solved with the Eviews software are presented.

Author: Antonio Jose Lopes Rodrigues
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Author: Vadim Smolyakov
Publisher: Simon and Schuster
ISBN: 1633439216
Category : Computers
Languages : en
Pages : 326

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Book Description
Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. Fully understanding how machine learning algorithms function is essential for any serious ML engineer. In Machine Learning Algorithms in Depth you’ll explore practical implementations of dozens of ML algorithms including: • Monte Carlo Stock Price Simulation • Image Denoising using Mean-Field Variational Inference • EM algorithm for Hidden Markov Models • Imbalanced Learning, Active Learning and Ensemble Learning • Bayesian Optimization for Hyperparameter Tuning • Dirichlet Process K-Means for Clustering Applications • Stock Clusters based on Inverse Covariance Estimation • Energy Minimization using Simulated Annealing • Image Search based on ResNet Convolutional Neural Network • Anomaly Detection in Time-Series using Variational Autoencoders Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probabilistic algorithms, you’ll learn the fundamentals of Bayesian inference and deep learning. You’ll also explore the core data structures and algorithmic paradigms for machine learning. Each algorithm is fully explored with both math and practical implementations so you can see how they work and how they’re put into action. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. This book guides you from the core mathematical foundations of the most important ML algorithms to their Python implementations, with a particular focus on probability-based methods. About the book Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics. You’ll especially appreciate author Vadim Smolyakov’s clear interpretations of Bayesian algorithms for Monte Carlo and Markov models. What's inside • Monte Carlo stock price simulation • EM algorithm for hidden Markov models • Imbalanced learning, active learning, and ensemble learning • Bayesian optimization for hyperparameter tuning • Anomaly detection in time-series About the reader For machine learning practitioners familiar with linear algebra, probability, and basic calculus. About the author Vadim Smolyakov is a data scientist in the Enterprise & Security DI R&D team at Microsoft. Table of Contents PART 1 1 Machine learning algorithms 2 Markov chain Monte Carlo 3 Variational inference 4 Software implementation PART 2 5 Classification algorithms 6 Regression algorithms 7 Selected supervised learning algorithms PART 3 8 Fundamental unsupervised learning algorithms 9 Selected unsupervised learning algorithms PART 4 10 Fundamental deep learning algorithms 11 Advanced deep learning algorithms

Author:
Publisher:
ISBN:
Category : Mechanics, Applied
Languages : en
Pages : 1380

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Author: Dang Nguyen
Publisher: Taylor & Francis
ISBN: 1003801978
Category : Social Science
Languages : en
Pages : 144

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Book Description
The computational turn in the social sciences and humanities has generated much excitement about the potential to refresh our approaches to the study of the techno-social. From natively digital to digitised data, researchers of digital diasporas increasingly find themselves working with a range of disparate digital objects. These digital objects can include anything from hyperlink to timestamps, from platform behavioural metrics such as react, share, or retweet to different media formats such as text, image, pre-recorded or livestreamed videos. Taking these disparate objects into account, this book introduces digital methods as research strategies not only for dealing with the ephemeral and unstable nature of tracing the diaspora with digital data, but also for reconceptualizing digital diasporas as assemblages and networks of more-than-human actors. The book also introduces a range of theoretical perspectives and methodological techniques to studying digital diasporas as contingent and processual hybrid collectives of heterogeneous material, cultural, and practice-based assemblages. This book will be essential reading for students and scholars interested in the digital space and transnational communities.

Author: DR ABHISHEK SHARMA
Publisher: JEC PUBLICATION
ISBN: 9361759922
Category : Law
Languages : en
Pages : 232

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Book Description
In an increasingly interconnected world, the landscape of international trade is evolving at an unprecedented pace. The complexities and nuances of global markets demand not only a deep understanding of traditional trade principles but also the agility to adapt to modern dynamics. "International Trade Dynamics: Advanced Strategies for Modern Markets" is crafted to bridge the gap between foundational trade theories and the advanced strategies necessary for navigating today’s volatile economic environment. This book aims to provide a comprehensive exploration of the multifaceted world of international trade. From the economic principles that underpin trade policies to the latest trends in global supply chain management, we delve into the critical factors shaping international commerce. Our goal is to equip readers with the knowledge and tools needed to formulate and implement effective trade strategies in a rapidly changing global market. We begin by revisiting the core concepts of international trade, setting the stage for a deeper dive into contemporary issues and advanced strategies. The chapters that follow cover a wide range of topics, including trade policy formulation, economic integration, digital trade, sustainability, and risk management. Each chapter is designed to offer a blend of theoretical insights and practical applications, supported by real-world case studies and empirical data. The emphasis on advanced strategies reflects the need for businesses and policymakers to think beyond traditional boundaries. In today’s markets, success hinges on the ability to leverage technological advancements, adapt to shifting geopolitical landscapes, and respond to the demands for sustainable and ethical trade practices. This book provides a roadmap for navigating these challenges, offering strategic guidance for stakeholders at all levels

Author: El Naqa, Issam
Publisher: IGI Global
ISBN: 152253086X
Category : Medical
Languages : en
Pages : 325

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Book Description
Cancer is a leading cause of death that affects numerous people at every age and their relatives. In recent years, there has been a tremendous advancement in imaging and biotechnology technologies and techniques for aiding in the detection, diagnosis, and treatment of cancer. Emerging Developments and Practices in Oncology provides research on recent advances in oncology aiming to improve early detection and personalized treatment of cancer. While highlighting applied methods of therapy, such as body radiotherapy, chemoradiotherapy, and immunotherapy, readers learn about the transforming approach to oncology in modern medicine and new technologies used to diagnose and treat cancer. This book is an important resource for medical trainees, graduate students, active practitioners, researchers, and clinical scientists seeking current research on oncology trends and applications.

Author: Priyanka Harjule
Publisher: CRC Press
ISBN: 1000831094
Category : Computers
Languages : en
Pages : 359

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Book Description
This book covers computational statistics-based approaches for Artificial Intelligence. The aim of this book is to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modelling and statistical techniques and describing their applications in different Artificial Intelligence systems. The primary users of this book will include researchers, academicians, postgraduate students, and specialists in the areas of data science, mathematical modelling, and Artificial Intelligence. It will also serve as a valuable resource for many others in the fields of electrical, computer, and optical engineering. The key features of this book are: Presents development of several real-world problem applications and experimental research in the field of computational statistics and mathematical modelling for Artificial Intelligence Examines the evolution of fundamental research into industrialized research and the transformation of applied investigation into real-time applications Examines the applications involving analytical and statistical solutions, and provides foundational and advanced concepts for beginners and industry professionals Provides a dynamic perspective to the concept of computational statistics for analysis of data and applications in intelligent systems with an objective of ensuring sustainability issues for ease of different stakeholders in various fields Integrates recent methodologies and challenges by employing mathematical modeling and statistical techniques for Artificial Intelligence