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It covers fundamental and advanced topics.\n\n## Key Topics Covered\n\n- **Limits and continuity:** understanding function behavior near points, formal definitions, and practical examples  \n- **Derivatives of functions:** basics of differentiation, power, product, quotient, and chain rules  \n- **Derivatives of common functions:** including sine and cosine, with examples and computational tools  \n- **Multivariate calculus:** partial derivatives, gradient vectors, Jacobian and Hessian matrices, and their significance in ML  \n- **Optimization fundamentals:** unconstrained and constrained optimization, Lagrange multipliers, and applications in ML models  \n- **Taylor series and approximations:** polynomial approximations of functions to simplify complex models  \n- **Gradient descent and learning:** iterative methods to minimize loss functions in ML  \n- **Calculus in neural networks:** understanding backpropagation and gradient computation for training  \n- **Support Vector Machines (SVM):** mathematical formulation and optimization using calculus tools  \n\nThis notebook combines theoretical explanations with practical examples to provide a solid foundation in calculus concepts that are critical for developing and understanding machine learning models.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fricardorobledo%2Fml_calculus","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fricardorobledo%2Fml_calculus","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fricardorobledo%2Fml_calculus/lists"}