Small Language Models with PyTorch : From Neural Network Fundamentals to Building Real-World AI Applications
Language: English
Published by Independently Published Jun 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 31.25
Quantity: 2 available
Add to basketItem description from seller
Neuware - From Neural Network Fundamentals to Building Real-World AI ApplicationsArtificial Intelligence is transforming the world but most developers only learn how to use AI models, not how to build them.Small Language Models with PyTorch is a practical, hands-on guide designed to help students, developers, researchers, and aspiring AI engineers master the foundations of modern Natural Language Processing by building language models completely from scratch. Rather than relying on black-box APIs or prebuilt frameworks, this book takes you deep into the inner workings of language models so you can truly understand how they function, how they are trained, and how they can be customized for real-world applications.Using the powerful PyTorch framework, this book walks you step-by-step through the entire lifecycle of creating Small Language Models (SLMs) from neural network fundamentals and tokenization to embeddings, attention mechanisms, Transformers, training pipelines, evaluation techniques, fine-tuning strategies, and deployment.Every chapter is designed with practical learning in mind. Complex concepts are broken down into clear explanations, intuitive analogies, visual diagrams, and hands-on coding exercises that make advanced AI topics approachable for beginners while still offering valuable depth for experienced developers and researchers.Inside this book, you will learn how to: - Understand the mathematics and intuition behind neural networks and deep learning- Build tokenizers and vocabulary systems from scratch- Create embeddings and positional encoding systems- Implement self-attention and Transformer architectures using PyTorch- Train, evaluate, and optimize Small Language Models efficiently- Fine-tune models for domain-specific NLP tasks- Build conversational AI and question-answering systems- Reduce overfitting and improve model performance- Deploy trained models into real-world applications- Understand the ethical implications, limitations, and societal impact of AI systemsThe book also includes a complete capstone project where you will build a fully functional domain-specific Question-Answering Bot with deployment considerations and production-focused insights.Whether you are a Computer Science student, an aspiring Machine Learning engineer, a software developer transitioning into AI, or a researcher looking for a practical implementation guide, this book provides the strong foundation needed to confidently build and experiment with modern language models.By the end of this journey, you will not only understand how language models work you will possess the practical skills to design, train, customize, and deploy your own intelligent AI systems from scratch using PyTorch.No magic. No black boxes. Just real understanding, practical coding, and modern AI engineering.…
Seller Inventory # 9798180851215
- Title
- Small Language Models with PyTorch : From Neural Network Fundamentals to Building Real-World AI Applications
- Author
- Isaac Rowan
- Publisher
- Independently Published Jun 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798180851215
- Item weight
- 304 grams
- Dimensions
- 254x178x9 mm
Artificial Intelligence is transforming the world but most developers only learn how to use AI models, not how to build them.
Small Language Models with PyTorch is a practical, hands-on guide designed to help students, developers, researchers, and aspiring AI engineers master the foundations of modern Natural Language Processing by building language models completely from scratch. Rather than relying on black-box APIs or prebuilt frameworks, this book takes you deep into the inner workings of language models so you can truly understand how they function, how they are trained, and how they can be customized for real-world applications.
Using the powerful PyTorch framework, this book walks you step-by-step through the entire lifecycle of creating Small Language Models (SLMs) from neural network fundamentals and tokenization to embeddings, attention mechanisms, Transformers, training pipelines, evaluation techniques, fine-tuning strategies, and deployment.
Every chapter is designed with practical learning in mind. Complex concepts are broken down into clear explanations, intuitive analogies, visual diagrams, and hands-on coding exercises that make advanced AI topics approachable for beginners while still offering valuable depth for experienced developers and researchers.
Inside this book, you will learn how to:
- Understand the mathematics and intuition behind neural networks and deep learning
- Build tokenizers and vocabulary systems from scratch
- Create embeddings and positional encoding systems
- Implement self-attention and Transformer architectures using PyTorch
- Train, evaluate, and optimize Small Language Models efficiently
- Fine-tune models for domain-specific NLP tasks
- Build conversational AI and question-answering systems
- Reduce overfitting and improve model performance
- Deploy trained models into real-world applications
- Understand the ethical implications, limitations, and societal impact of AI systems
Whether you are a Computer Science student, an aspiring Machine Learning engineer, a software developer transitioning into AI, or a researcher looking for a practical implementation guide, this book provides the strong foundation needed to confidently build and experiment with modern language models.
By the end of this journey, you will not only understand how language models work you will possess the practical skills to design, train, customize, and deploy your own intelligent AI systems from scratch using PyTorch.
No magic. No black boxes. Just real understanding, practical coding, and modern AI engineering.
"Synopsis" may belong to another edition of this title.
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