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The book provides a structured introduction to core areas of artificial intelligence, including machine learning, deep learning, large language models, and agent-based systems. It combines theoretical foundations with practical implementation, using tools such as scikit-learn, Keras, and Ollama. Each chapter includes code examples, exercises, and links to interactive notebooks. The focus lies on conveying applicable knowledge for real-world use. The book is intended for students and practitioners in computer science or related fields and can be used for teaching, self-study, or reference in applied contexts.
The book provides a structured introduction to core areas of artificial intelligence, including machine learning, deep learning, large language models, and agent-based systems. It combines theoretical foundations with practical implementation, using tools such as scikit-learn, Keras, and Ollama. Each chapter includes code examples, exercises, and links to interactive notebooks. The focus lies on conveying applicable knowledge for real-world use. The book is intended for students and practitioners in computer science or related fields and can be used for teaching, self-study, or reference in applied contexts.
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