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Shallow Learning vs. Deep Learning A Practical Guide for Machine Learning Solutions - Omer Faruk Ertugrul, Josep M Guerrero, Musa Yilmaz 2024 PDF | EPUB Springer BOOKS
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Shallow Learning vs. Deep Learning A Practical Guide for Machine Learning Solutions
Author: Omer Faruk Ertugrul, Josep M Guerrero, Musa Yilmaz
Year: 2024
Format: PDF | EPUB
File size: 45.5 MB
Language: ENG



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Shallow Learning vs Deep Learning A Practical Guide for Machine Learning Solutions In this book, we explore the differences between shallow learning and deep learning, two fundamental approaches to machine learning that have revolutionized the field of artificial intelligence. We delve into the strengths and weaknesses of each approach, discussing their applications, advantages, and limitations. We also examine the current state of the field, including recent advances and future trends, and provide practical guidance on how to choose the right approach for your specific needs. Whether you're a seasoned practitioner or just starting out, this guide will help you navigate the complex world of machine learning and make informed decisions about your projects. The book begins by defining the terms "shallow learning" and "deep learning," explaining the key concepts and techniques associated with each approach. We then dive into the history of these methods, tracing their development and evolution over time. Next, we examine the similarities and differences between shallow and deep learning, highlighting the unique characteristics of each approach and how they are applied in real-world scenarios. The heart of the book focuses on the practical aspects of shallow and deep learning, providing step-by-step instructions for implementing these methods in your own projects. We cover a range of topics, from data preparation and model selection to training and evaluation, offering tips and tricks for achieving optimal results.
Shallow arning vs Deep arning Практическое руководство по решениям для машинного обучения В этой книге мы исследуем различия между поверхностным обучением и глубоким обучением, двумя фундаментальными подходами к машинному обучению, которые произвели революцию в области искусственного интеллекта. Мы углубляемся в сильные и слабые стороны каждого подхода, обсуждая их применение, преимущества и ограничения. Мы также изучаем текущее состояние этой области, включая последние достижения и будущие тенденции, и предоставляем практические рекомендации о том, как выбрать правильный подход к вашим конкретным потребностям. Независимо от того, являетесь ли вы опытным практиком или только начинаете, это руководство поможет вам ориентироваться в сложном мире машинного обучения и принимать обоснованные решения о ваших проектах. Книга начинается с определения терминов «поверхностное обучение» и «глубокое обучение», объясняющих ключевые понятия и техники, связанные с каждым подходом. Затем мы погружаемся в историю этих методов, прослеживая их развитие и эволюцию во времени. Затем мы рассмотрим сходства и различия между поверхностным и глубоким обучением, выделив уникальные характеристики каждого подхода и то, как они применяются в реальных сценариях. В основе книги - практические аспекты неглубокого и глубокого обучения, дающие пошаговые инструкции по внедрению этих методов в собственные проекты. Мы охватываем целый ряд тем, от подготовки данных и выбора модели до обучения и оценки, предлагая советы и рекомендации для достижения оптимальных результатов.
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