Multivariable Calculus and Gradient Descent
Multivariable calculus for AI. Learn to implement Gradient Descent, understand optimization landscapes, and use Python to train ML models.
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AI Engineering Volume 1
Derivatives, Partial Derivatives, and the Chain Rule
Calculus foundations of AI, covering derivatives, partial derivatives, the chain rule, and gradient descent with Python examples in TensorFlow.
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Eigenvalues, Eigenvectors, and Matrix Decomp. Applications
Master eigenvalues, eigenvectors, and Principal Component Analysis (PCA). This chapter covers the theory, practical Python implementation.
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Advanced Matrix Operations and Decomposition
Advanced matrix decomposition (LU, QR, Cholesky) for AI and machine learning. Learn theory, Python implementation, and real-world applications.
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Basic Matrix Operations and Properties
Fundamental matrix operations in AI. Learn matrix multiplication, addition, and transpose with Python/NumPy examples and real-world applications.
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Vector Spaces and Linear Independence
Vector spaces, linear independence, and basis in AI. Learn the mathematical foundations and Python for dimensionality reduction and feature engineering.
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Vector Fundamentals and Operations
Vector fundamentals for AI engineering. Learn to implement vector addition, dot products, and cosine similarity in Python with NumPy.
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Ethics and Responsible AI Development
AI ethics and responsible development. Analyze and mitigate bias, implement fairness, and build accountable AI systems using modern frameworks and tools.
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AI Industry Landscape and Career Opportunities
Explore the dynamic AI industry landscape, key market trends, and diverse career opportunities. A comprehensive guide for students and professionals.
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AI, Machine Learning & Deep Learning: A Concept Hierarchy
Clarify the distinctions between AI, Machine Learning, and Deep Learning. Understand the hierarchy, applications, and trade-offs.
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