BOOKS - Machine Learning Approaches in Financial Analytics
Machine Learning Approaches in Financial Analytics - Leandros A. Maglaras, Sonali Das, Naliniprava Tripathy, Srikanta Patnaik 2024 PDF | EPUB Springer BOOKS
ECO~18 kg CO²

1 TON

Views
25916

Telegram
 
Machine Learning Approaches in Financial Analytics
Author: Leandros A. Maglaras, Sonali Das, Naliniprava Tripathy, Srikanta Patnaik
Year: 2024
Pages: 485
Format: PDF | EPUB
File size: 53.1 MB
Language: ENG



Pay with Telegram STARS
The book "Machine Learning Approaches in Financial Analytics" explores the use of machine learning techniques in financial analytics, providing insights into how these methods can be applied to real-world problems. The book covers topics such as supervised and unsupervised learning, deep learning, natural language processing, and reinforcement learning, and their applications in finance. It also discusses the challenges and limitations of these approaches and provides practical examples of their implementation in financial institutions. The author emphasizes the importance of understanding the process of technological evolution and its impact on society, arguing that this understanding is essential for the survival of humanity and the unity of people in a world torn apart by conflict. He suggests that developing a personal paradigm for perceiving the technological process of developing modern knowledge is crucial for navigating the complex and rapidly changing landscape of technology. The book begins with an introduction to machine learning and its relevance to financial analytics, highlighting the need for a comprehensive understanding of the field. The author then delves into the various machine learning approaches and their applications in finance, including predictive modeling, risk management, and portfolio optimization. The book also covers the challenges associated with implementing machine learning in finance, such as data quality issues and the need for domain expertise.
Книга «Подходы машинного обучения в финансовой аналитике» исследует использование методов машинного обучения в финансовой аналитике, предоставляя понимание того, как эти методы могут быть применены к реальным проблемам. Книга охватывает такие темы, как контролируемое и неконтролируемое обучение, глубокое обучение, обработка естественного языка и обучение с подкреплением, а также их применение в финансах. В нем также обсуждаются проблемы и ограничения этих подходов и приводятся практические примеры их реализации в финансовых учреждениях. Автор подчеркивает важность понимания процесса технологической эволюции и его влияния на общество, утверждая, что это понимание необходимо для выживания человечества и единства людей в мире, раздираемом конфликтами. Он предполагает, что разработка личной парадигмы восприятия технологического процесса развития современных знаний имеет решающее значение для навигации по сложному и быстро меняющемуся ландшафту технологий. Книга начинается с введения в машинное обучение и его соответствия финансовой аналитике, подчеркивая необходимость всестороннего понимания этой области. Затем автор углубляется в различные подходы машинного обучения и их применения в финансах, включая прогнозное моделирование, управление рисками и оптимизацию портфеля. Книга также охватывает проблемы, связанные с внедрением машинного обучения в финансах, такие как вопросы качества данных и необходимость экспертизы в области.
''

You may also be interested in:

Machine Learning in Production: Master the art of delivering robust Machine Learning solutions with MLOps (English Edition)
Serverless Machine Learning with Amazon Redshift ML: Create, train, and deploy machine learning models using familiar SQL commands
Applied Machine Learning and High-Performance Computing on AWS: Accelerate the development of machine learning applications following architectural best practices
Machine Learning Master Machine Learning Fundamentals for Beginners, Business Leaders and Aspiring Data Scientists
Machine Learning for Data Streams with Practical Examples in MOA (Adaptive Computation and Machine Learning series)
Online Machine Learning: A Practical Guide with Examples in Python (Machine Learning: Foundations, Methodologies, and Applications)
Robust Machine Learning: Distributed Methods for Safe AI (Machine Learning: Foundations, Methodologies, and Applications)
Machine Learning with Core ML 2 and Swift A beginner-friendly guide to integrating machine learning into your apps
Machine Learning: A Guide to PyTorch, TensorFlow, and Scikit-Learn: Mastering Machine Learning With Python
Machine Learning A Guide to PyTorch, TensorFlow, and Scikit-Learn Mastering Machine Learning With Python
Machine Learning A Guide to PyTorch, TensorFlow, and Scikit-Learn Mastering Machine Learning With Python
Machine Learning for Beginners Build and deploy Machine Learning systems using Python, 2nd Edition
Programming Machine Learning Machine Learning Basics Concepts + Artificial Intelligence + Python Programming + Python Machine Learning
Programming Machine Learning Machine Learning Basics Concepts + Artificial Intelligence + Python Programming + Python Machine Learning
Computer Programming This Book Includes Machine Learning for Beginners, Machine Learning with Python, Deep Learning with Python, Python for Data Analysis
Programming With Python 4 Manuscripts - Deep Learning With Keras, Convolutional Neural Networks In Python, Python Machine Learning, Machine Learning With Tensorflow
Pragmatic Machine Learning with Python Learn How to Deploy Machine Learning Models in Production
Machine Learning, Animated (Chapman and Hall CRC Machine Learning and Pattern Recognition)
Machine Learning for Beginners A Practical Guide to Understanding and Applying Machine Learning Concepts
Machine Learning for Absolute Beginners An Absolute beginner’s guide to learning and understanding machine learning successfully
Machine Learning with Python The Ultimate Guide to Learn Machine Learning Algorithms. Includes a Useful Section about Analysis, Data Mining and Artificial Intelligence in Business Applications
Machine Learning Tutorial: Machine Learning Simply Easy Learning
Cloud Computing for Machine Learning and Cognitive Applications A Machine Learning Approach
Machine Learning Interviews Kickstart Your Machine Learning and Data Career (Final)
Machine Learning Production Systems Engineering Machine Learning Models and Pipelines
Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
Introduction to Machine Learning (Adaptive Computation and Machine Learning), 4th Edition
Machine Learning An In-Depth Beginners Guide into the Essentials of Machine Learning Algorithms
Statistics for Machine Learning Implement Statistical methods used in Machine Learning using Python
Python Machine Learning A Complete Guide for Beginners on Machine Learning and Deep Learning with Python
Python Machine Learning Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow
Practical Machine Learning with R and Python Machine Learning in Stereo, Third Edition
Machine Learning for Beginners An Introduction to Artificial Intelligence and Machine Learning
Machine Learning Interviews: Kickstart Your Machine Learning and Data Career
Unobtrusive Observations of Learning in Digital Environments: Examining Behavior, Cognition, Emotion, Metacognition and Social Processes Using Learning … in Analytics for Learning and Teaching)
Ultimate MLOps for Machine Learning Models Use Real Case Studies to Efficiently Build, Deploy, and Scale Machine Learning Pipelines with MLOps
Ultimate MLOps for Machine Learning Models Use Real Case Studies to Efficiently Build, Deploy, and Scale Machine Learning Pipelines with MLOps
Machine Learning For Beginners A Math Free Introduction for Business and Individuals to Machine Learning, Big Data, Data Science, and Neural Networks
Unsupervised Machine Learning in Python Master Data Science and Machine Learning with Cluster Analysis, Gaussian Mixture Models, and Principal Components Analysis