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Visual Maths for ML: Calculus
An illustrated guide to change, gradients and machine learning
This book is 100% completeLast updated on 2026-10-18
Understand calculus through annotated diagrams and a delivery-time prediction example. Explore derivatives, gradients, backpropagation and integrals. Includes PDF and fixed-layout EPUB.
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About
About the Book
Calculus, made visual.
Understand how a small change becomes a better prediction. Follow Aarav and Maya through annotated diagrams, worked calculations and a delivery-time example that connects calculus to machine learning.
What you will learn
• Functions, rates of change, limits, derivatives and tangent-line approximations.
• Power, product, quotient and chain rules; exponentials and logarithms.
• Squared loss, gradient descent and learning rates.
• Partial derivatives, gradients, directional derivatives and the multivariable chain rule.
• Backpropagation, computation graphs, neural-network units and ReLU.
• Jacobians, Hessians, stationary points, Taylor approximations and series.
• Finite differences, automatic differentiation and gradient checking.
• Integrals, accumulation and the fundamental theorem of calculus.
• Sigmoid probabilities, classification loss and its derivatives.
Included
A 45-page illustrated PDF and fixed-layout EPUB: a cover plus 44 lesson and reference pages, including a roadmap, worked examples and checks with answers. The EPUB includes lesson navigation.
Download the free sample to explore the calculus roadmap and reference map.
Designed for visual learners with basic school-level algebra. This is a conceptual maths guide, not a coding course or a complete university calculus textbook. The fixed-layout EPUB preserves the illustrations; text does not reflow, and small screens may require zooming. Use an EPUB reader that supports fixed layouts.
By Pramod Tyagi · www.bitmonkey.ai
© 2026 Bitmonkey IT Services Pvt Ltd. All rights reserved.
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Author
About the Author
Pramod Tyagi
Pramod Kumar Tyagi is an AI and digital architect with more than 20 years of experience designing and scaling enterprise technology platforms. He is the founder of Bitmonkey IT Services Pvt Ltd, where his work spans AI products, digital transformation and interactive AI/ML education.
Previously, Pramod served as Principal Architect and Head of Enterprise Integration & AI Centre of Excellence at NTT DATA Services. His experience includes AI and machine learning, cloud architecture, data platforms and intelligent automation across banking, payments, healthcare, retail and supply chain.
He holds an MS in Aerospace Engineering from the Indian Institute of Science (IISc), Bangalore, and is a TOGAF-certified solution architect.
His Visual Maths for ML series uses annotated diagrams, worked examples and practical scenarios to help readers build intuition for the mathematics behind machine learning.
Explore his work at www.bitmonkey.ai.
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