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Python Derivatives Simplified
Mastering the Art of Computing Derivatives with Python
Premium AI Book - 200+ pages
Unlock the Power of Python for Computing Derivatives
Delve into the dynamic world of calculus with Python Derivatives Simplified, where mathematics meets programming to create a potent computational tool. Whether you're a beginner eager to learn the basics, or an expert looking to refine your techniques, this 12-chapter guide offers comprehensive coverage of computing derivatives using Python.
Explore the synergy between Python's powerful libraries and the analytical prowess of calculus. With clear explanations suitable for novices and intricate detail that will satisfy the experts, each chapter is crafted to systematically unfold the complexities of computing derivatives.
A practical and educational journey awaits. Experience hands-on learning as you tackle real-world applications, benefit from unique perspectives, and leverage Python's capabilities to solve complex derivative problems.
Amplify your knowledge and skills, and add an invaluable asset to your professional toolkit. Let Python Derivatives Simplified be your guide to mastering the mathematical foundations, enhancing your computational fluency, and achieving analytical brilliance.
Attributes include:
- Accessible tutorials for beginners
- Advanced concepts for seasoned practitioners
- Insightful analyses and practical applications
Table of Contents
1. Introduction to Python & Calculus- Python Programming Foundations
- Basic Principles of Calculus
- Setting the Stage for Computational Calculus
2. Fundamentals of Derivatives
- Understanding Rates of Change
- The Power of the Derivative
- Derivatives in Real-World Contexts
3. Python Libraries for Calculus
- Leveraging NumPy
- Exploring SciPy's Capabilities
- Utilizing SymPy for Symbolic Mathematics
4. Computing Derivatives in Python
- Writing Derivative Functions
- Solving Derivative Problems Using Python
- Error Analysis and Troubleshooting
5. Applied Derivatives in Data Analysis
- Gradient Descent for Optimization
- Curvature and Inflection Points in Data
- Case Studies: Derivatives in Action
6. Mathematical Theory Behind Derivatives
- Limits and Continuity
- Rules and Methods of Differentiation
- Understanding Higher-Order Derivatives
7. Advanced Derivative Techniques
- Partial Derivatives and Multivariable Functions
- Using Derivatives in Integral Calculus
- Exploring Taylor Series and Approximations
8. Visualization of Derivatives
- Graphical Interpretations with Matplotlib
- Animating Derivatives for Better Comprehension
- Interactive Plots with Plotly
9. Algorithmic Differentiation
- Introduction to Automatic Differentiation
- Implementing Backpropagation in Neural Networks
- Performance and Efficiency in AD Systems
10. Numerical Methods and Simulations
- Finite Differences and Estimations
- Simulating Dynamical Systems
- Stochastic Derivatives and Monte Carlo Methods
11. Derivatives in Machine Learning
- Optimizing with Gradient-Based Learning
- Derivatives in Loss Function Minimization
- Advanced Algorithms and Derivative Applications
12. Putting it All Together
- Building Projects from Scratch
- Best Practices and Coding Standards
- Future Trends in Computational Derivatives
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