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Neural Networks for Beginners: Build, Train, and Deploy AI Models Using Python, Machine Learning, and Deep Learning
Paperback
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ISBN13: 9798258514820
Publisher: Independently Published
Published: Apr 22 2026
Pages: 122
Weight: 0.38
Height: 0.26 Width: 6.00 Depth: 9.00
Language: English
Publisher: Independently Published
Published: Apr 22 2026
Pages: 122
Weight: 0.38
Height: 0.26 Width: 6.00 Depth: 9.00
Language: English
Artificial Intelligence is no longer the future-it is the present. From recommendation systems and self-driving cars to chatbots and financial forecasting, neural networks are powering the world's most transformative technologies. Yet for many beginners, the field can feel complex, overwhelming, and difficult to navigate.
Neural Networks for Beginners is your clear, structured, and practical guide to understanding and building intelligent systems from the ground up. Designed for aspiring developers, students, and professionals, this book breaks down complex concepts into accessible explanations while maintaining the depth needed to build real-world applications.
Inside this book, you will move beyond theory and learn how neural networks actually work-mathematically, conceptually, and in code. Starting with the fundamentals, you will gradually progress to advanced architectures and deployment strategies, gaining hands-on experience with tools used by modern AI engineers.
You will learn how to:
Understand the core principles of neural networks, including neurons, layers, and activation functions, and how they work together to model complex patterns
Build and train neural networks from scratch using Python, gaining a deep understanding of forward propagation and backpropagation
Work with modern deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers
Optimize model performance using advanced training techniques, hyperparameter tuning, and regularization strategies
Evaluate models effectively using industry-standard metrics and interpret results with confidence
Apply transfer learning to leverage pre-trained models and accelerate your projects
Deploy neural networks into real-world applications using APIs, containers, and scalable infrastructure
Unlike many introductory books that remain purely theoretical, this guide emphasizes practical implementation and real-world relevance. Each concept is supported with clear explanations and working Python examples, helping you build confidence as you progress.
By the end of this book, you will not only understand how neural networks work-you will be able to design, train, evaluate, and deploy your own AI models with confidence.
Whether you are starting your journey in artificial intelligence or looking to strengthen your foundation, Neural Networks for Beginners provides the tools, knowledge, and mindset needed to succeed in today's AI-driven world.
Why This Book
Beginner-friendly explanations without sacrificing technical depth
Hands-on Python examples for real-world learning
Covers the full AI pipeline from fundamentals to deployment
Designed for developers, students, and aspiring AI engineers
Inside this book, you will move beyond theory and learn how neural networks actually work-mathematically, conceptually, and in code. Starting with the fundamentals, you will gradually progress to advanced architectures and deployment strategies, gaining hands-on experience with tools used by modern AI engineers.
You will learn how to:
Understand the core principles of neural networks, including neurons, layers, and activation functions, and how they work together to model complex patterns
Build and train neural networks from scratch using Python, gaining a deep understanding of forward propagation and backpropagation
Work with modern deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers
Optimize model performance using advanced training techniques, hyperparameter tuning, and regularization strategies
Evaluate models effectively using industry-standard metrics and interpret results with confidence
Apply transfer learning to leverage pre-trained models and accelerate your projects
Deploy neural networks into real-world applications using APIs, containers, and scalable infrastructure
Unlike many introductory books that remain purely theoretical, this guide emphasizes practical implementation and real-world relevance. Each concept is supported with clear explanations and working Python examples, helping you build confidence as you progress.
By the end of this book, you will not only understand how neural networks work-you will be able to design, train, evaluate, and deploy your own AI models with confidence.
Whether you are starting your journey in artificial intelligence or looking to strengthen your foundation, Neural Networks for Beginners provides the tools, knowledge, and mindset needed to succeed in today's AI-driven world.
Why This Book
Beginner-friendly explanations without sacrificing technical depth
Hands-on Python examples for real-world learning
Covers the full AI pipeline from fundamentals to deployment
Designed for developers, students, and aspiring AI engineers
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