Название: Fundamentals of Machine Learning: An Introduction to Neural NetworksАвтор: Peter Johnson
Издательство: HiTeX Press
Год: 2024
Страниц: 542
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB
"Fundamentals of Machine Learning: An Introduction to Neural Networks" is an accessible yet comprehensive guide designed for individuals new to the intriguing world of Machine Learning. This book meticulously unfolds the foundational principles and techniques in Machine Learning while placing a keen emphasis on neural networks. Readers are led through a structured journey from basic definitions and historical context to more complex concepts, ensuring a clear and thorough understanding of the subject.
Each chapter is dedicated to core topics such as data preprocessing, activation functions, model training, evaluation metrics, and advanced neural network architectures. The book also explores practical applications across various domains, highlighting how neural networks resolve real-world problems. With a focus on making complex topics digestible, this book serves as an invaluable resource for beginners aspiring to grasp the essentials of Machine Learning and neural networks, equipping them with the knowledge to apply these techniques effectively.
Machine Learning techniques can be broadly categorized into supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Supervised learning involves training models on labeled data, where each input is paired with the corresponding output. Unsupervised learning, on the other hand, deals with unlabeled data and aims to uncover hidden patterns or structures within the data. Semi-supervised learning combines aspects of both supervised and unsupervised learning, utilizing a small amount of labeled data alongside a large corpus of unlabeled data. Reinforcement learning, a widely studied paradigm, involves training agents to make a sequence of decisions by interacting with an environment and receiving feedback in the form of rewards or penalties.
Central to the field of Machine Learning are neural networks, computational models inspired by the structure and function of the human brain. Neural networks consist of layers of interconnected neurons, each capable of performing simple computations. When combined, these neurons can approximate complex functions and solve intricate problems. The success of neural networks in various applications, such as image recognition, natural language processing, and game playing, has spurred extensive research and development in this area.
A key aspect of Machine Learning is data preprocessing and feature engineering, which involves transforming raw data into a format suitable for model training. This process includes handling missing values, encoding categorical variables, scaling numerical features, and extracting meaningful information from the data. Proper data preprocessing is crucial for the success of Machine Learning models, as it directly impacts their performance and generalization ability.
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