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Calculus Essentials for Machine Learning Chapter 1: Introduction to Calculus in Machine Learning Why Calculus Matters in ML Review of Basic Calculus Concepts Chapter 2: Derivatives and Their Applications Understanding Derivatives Gradient and Cost Functions Chapter 3: Integrals and Area Under the Curve Definite and Indefinite Integrals Applications in Probability Chapter 4: Multivariable Calculus Functions of Several Variables Partial Derivatives and Gradients Chapter 5: Optimization in Machine Learning Introduction to Optimization Gradient Descent Algorithms Advanced Optimization Techniques Chapter 6: Conclusion and Future Directions Summary of Key Concepts Further Study Recommendations

Introduction to Optimization

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