Before Machine Learning Volume 2 - Calculus for A.I

Deepen your calculus foundation for AI and machine learning with essential concepts like derivatives, integrals, and multivariable calculus, all applied directly to neural networks and optimization.

Key Features

• A step-by-step guide to calculus concepts tailored for AI and machine learning applications
• Clear explanations of advanced topics like Taylor Series, gradient descent, and backpropagation
• Practical insights connecting calculus principles directly to neural networks and data science

Book Description

What you will learn

• Explore the essentials of calculus for machine learning
• Calculate derivatives and apply them in optimization tasks
• Analyze functions, limits, and continuity in data science
• Apply Taylor Series for predictive curve modeling
• Use gradient descent for effective cost-minimization
• Implement multivariable calculus in neural networks

Who this book is for

Aspiring AI engineers, machine learning students, and data scientists will find this book valuable for building a strong calculus foundation. A basic understanding of calculus is beneficial, but the book introduces essential concepts gradually for all levels.