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Название: Build Your Own Neural Networks: Step-By-Step Explanation For Beginners
Автор: Kilho Shin
Издательство: Independently published
Год: 2024
Страниц: 210
Язык: английский
Формат: epub
Размер: 10.1 MB
Are you curious about how neural networks work and want to build your own from scratch? "Build Your Own Neural Networks" is the perfect guide for beginners looking to dive into the fascinating world of Artificial Intelligence (AI). "Build Your Own Neural Networks: Step-By-Step Explanation For Beginners" is designed to demystify the complexities of neural networks for those who are new to the field of Deep Learning. This book is your comprehensive guide to understanding and implementing neural networks from the ground up, using the PyTorch framework. Each chapter is structured to provide hands-on experience with practical code examples, detailed step-by-step explanations, and engaging mini-projects that ensure a practical understanding of the concepts discussed. This approach not only enhances learning but also makes the journey fun and interactive. By avoiding complex jargon and focusing on clear, simple explanations, we ensure that you gain a solid foundation in neural networks without feeling overwhelmed. To break down the abstract world of Neural Networks, we will start with one concrete tool- Python, amongst the most popular languages for Machine Learning (ML) and Deep Learning (DL) development. To best exploit Python’s various capabilities, an understanding of some fundamental concepts and functions is essential. This book is ideal for anyone who has a basic understanding of programming and a curiosity about AI and ML. Whether you are a student, a hobbyist, or a professional looking to switch careers, this book will equip you with the knowledge you need to start building your own neural networks. It's especially useful for readers who prefer learning by doing and enjoy hands-on projects that reinforce learning. No prior experience with Deep Learning frameworks is required, as we start from the very basics and gradually progress to more complex topics.
Автор: Kilho Shin
Издательство: Independently published
Год: 2024
Страниц: 210
Язык: английский
Формат: epub
Размер: 10.1 MB
Are you curious about how neural networks work and want to build your own from scratch? "Build Your Own Neural Networks" is the perfect guide for beginners looking to dive into the fascinating world of Artificial Intelligence (AI). "Build Your Own Neural Networks: Step-By-Step Explanation For Beginners" is designed to demystify the complexities of neural networks for those who are new to the field of Deep Learning. This book is your comprehensive guide to understanding and implementing neural networks from the ground up, using the PyTorch framework. Each chapter is structured to provide hands-on experience with practical code examples, detailed step-by-step explanations, and engaging mini-projects that ensure a practical understanding of the concepts discussed. This approach not only enhances learning but also makes the journey fun and interactive. By avoiding complex jargon and focusing on clear, simple explanations, we ensure that you gain a solid foundation in neural networks without feeling overwhelmed. To break down the abstract world of Neural Networks, we will start with one concrete tool- Python, amongst the most popular languages for Machine Learning (ML) and Deep Learning (DL) development. To best exploit Python’s various capabilities, an understanding of some fundamental concepts and functions is essential. This book is ideal for anyone who has a basic understanding of programming and a curiosity about AI and ML. Whether you are a student, a hobbyist, or a professional looking to switch careers, this book will equip you with the knowledge you need to start building your own neural networks. It's especially useful for readers who prefer learning by doing and enjoy hands-on projects that reinforce learning. No prior experience with Deep Learning frameworks is required, as we start from the very basics and gradually progress to more complex topics.