Items related to Mathematics For Artificial Intelligence (AI and ML...
Mathematics For Artificial Intelligence (AI and ML Reference handbooks) - Softcover
Book 13 of 22: AI and ML Reference handbooks
Synopsis
Mathematics for Artificial Intelligence is a practical, beginner-friendly guide that explains the mathematical foundations behind modern Artificial Intelligence and Machine Learning—without assuming an advanced mathematics background.
Instead of presenting formulas without context, this book explains why each mathematical concept matters, how it works, and where it is used in real AI systems. Every topic is connected to real-world machine learning applications, making it easier to understand not only the theory but also its practical significance.
Starting with the fundamentals of linear algebra and calculus, the book gradually builds toward probability, statistics, optimization, information theory, and the mathematics that powers deep learning. Concepts are explained using intuitive examples, visual diagrams, worked examples, and practical AI scenarios.
Whether you are preparing for AI interviews, pursuing university studies, building machine learning models, or transitioning into AI engineering, this book provides a clear roadmap through the mathematics used by today's intelligent systems.
Inside You'll Learn• Linear Algebra for Machine Learning
• Vectors, Matrices, Eigenvalues, Eigenvectors, SVD & PCA
• Calculus, Derivatives, Gradients & Chain Rule
• Probability, Random Variables & Distributions
• Statistics, Hypothesis Testing & Inference
• Bayesian Thinking and Bayes' Theorem
• Regression, Classification & Loss Functions
• Optimization, Gradient Descent & Convexity
• Information Theory, Entropy & KL Divergence
• Neural Networks, Backpropagation & Deep Learning Mathematics
• Attention Mechanisms and Transformer Mathematics
• Practical AI and Machine Learning Applications
• Worked Examples, Diagrams and Practice Questions
- AI and Machine Learning beginners
- Data Science students
- Computer Science and Engineering students
- Software Engineers transitioning into AI
- AI Professionals seeking stronger mathematical foundations
- Interview preparation for AI/ML roles
- Self-learners who prefer intuitive explanations over abstract proofs
If you've ever wondered "What mathematics actually powers Artificial Intelligence?"—this book provides the complete answer in a clear, practical, and engaging way.
"synopsis" may belong to another edition of this title.
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Mathematics For Artificial Intelligence (AI and ML Reference handbooks)
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Mathematics For Artificial Intelligence
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Mathematics For Artificial Intelligence (Paperback)
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Paperback. Condition: new. Paperback. Mathematics for Artificial Intelligence is a practical, beginner-friendly guide that explains the mathematical foundations behind modern Artificial Intelligence and Machine Learning-without assuming an advanced mathematics background.Instead of presenting formulas without context, this book explains why each mathematical concept matters, how it works, and where it is used in real AI systems. Every topic is connected to real-world machine learning applications, making it easier to understand not only the theory but also its practical significance.Starting with the fundamentals of linear algebra and calculus, the book gradually builds toward probability, statistics, optimization, information theory, and the mathematics that powers deep learning. Concepts are explained using intuitive examples, visual diagrams, worked examples, and practical AI scenarios.Whether you are preparing for AI interviews, pursuing university studies, building machine learning models, or transitioning into AI engineering, this book provides a clear roadmap through the mathematics used by today's intelligent systems.Inside You'll Learn- Linear Algebra for Machine Learning- Vectors, Matrices, Eigenvalues, Eigenvectors, SVD & PCA- Calculus, Derivatives, Gradients & Chain Rule- Probability, Random Variables & Distributions- Statistics, Hypothesis Testing & Inference- Bayesian Thinking and Bayes' Theorem- Regression, Classification & Loss Functions- Optimization, Gradient Descent & Convexity- Information Theory, Entropy & KL Divergence- Neural Networks, Backpropagation & Deep Learning Mathematics- Attention Mechanisms and Transformer Mathematics- Practical AI and Machine Learning Applications- Worked Examples, Diagrams and Practice QuestionsPerfect ForAI and Machine Learning beginnersData Science studentsComputer Science and Engineering studentsSoftware Engineers transitioning into AIAI Professionals seeking stronger mathematical foundationsInterview preparation for AI/ML rolesSelf-learners who prefer intuitive explanations over abstract proofsIf you've ever wondered "What mathematics actually powers Artificial Intelligence?"-this book provides the complete answer in a clear, practical, and engaging way. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798189885471
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Mathematics For Artificial Intelligence
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Taschenbuch. Condition: Neu. Neuware - Mathematics for Artificial Intelligence is a practical, beginner-friendly guide that explains the mathematical foundations behind modern Artificial Intelligence and Machine Learning-without assuming an advanced mathematics background.Instead of presenting formulas without context, this book explains why each mathematical concept matters, how it works, and where it is used in real AI systems. Every topic is connected to real-world machine learning applications, making it easier to understand not only the theory but also its practical significance.Starting with the fundamentals of linear algebra and calculus, the book gradually builds toward probability, statistics, optimization, information theory, and the mathematics that powers deep learning. Concepts are explained using intuitive examples, visual diagrams, worked examples, and practical AI scenarios.Whether you are preparing for AI interviews, pursuing university studies, building machine learning models, or transitioning into AI engineering, this book provides a clear roadmap through the mathematics used by today's intelligent systems.Inside You'll Learn- Linear Algebra for Machine Learning- Vectors, Matrices, Eigenvalues, Eigenvectors, SVD & PCA- Calculus, Derivatives, Gradients & Chain Rule- Probability, Random Variables & Distributions- Statistics, Hypothesis Testing & Inference- Bayesian Thinking and Bayes' Theorem- Regression, Classification & Loss Functions- Optimization, Gradient Descent & Convexity- Information Theory, Entropy & KL Divergence- Neural Networks, Backpropagation & Deep Learning Mathematics- Attention Mechanisms and Transformer Mathematics- Practical AI and Machine Learning Applications- Worked Examples, Diagrams and Practice QuestionsPerfect For- AI and Machine Learning beginners- Data Science students- Computer Science and Engineering students- Software Engineers transitioning into AI- AI Professionals seeking stronger mathematical foundations- Interview preparation for AI/ML roles- Self-learners who prefer intuitive explanations over abstract proofsIf you've ever wondered 'What mathematics actually powers Artificial Intelligence '-this book provides the complete answer in a clear, practical, and engaging way. Seller Inventory # 9798189885471
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