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Visual Maths for ML: Information Theory
Measure uncertainty. Compare predictions.
This book is 100% completeLast updated on 2026-10-19
Measure uncertainty. Compare predictions. Annotated diagrams and worked examples. Includes PDF and EPUB.
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About
About the Book
Measure uncertainty. Compare predictions. Build intuition through annotated diagrams, small worked examples and step-by-step explanations.
WHAT YOU WILL LEARN
1. Probability and information
Probability reports, next-token predictions, weighted averages, exponentials, logarithms and fractional powers.
2. Surprisal and units
Information as negative log probability; bits versus nats; why independent-event information adds and how rare events carry more information.
3. Entropy and coding
Expected surprisal, worked entropy calculations, binary entropy and its maximum, and prefix codes as a way to represent outcomes.
4. Score a prediction
Cross-entropy, observed-label negative log-likelihood, products of likelihoods and average log loss; comparing probability forecasts.
5. Compare distributions
KL divergence, its relationship to cross-entropy and entropy, why direction matters, and the consequence of assigning zero probability to possible outcomes.
6. Information shared by variables
Joint entropy, conditional entropy and mutual information, with a small weather dataset and step-by-step calculations.
7. Use information in ML
Decision-tree information gain, perplexity for predictions, and the reconstruction-plus-KL objective in a variational autoencoder.
8. Check understanding
Calibration, interpreting probability quality, worked numerical examples and practice that connects the measures.
WHAT IS INCLUDED
34-page PDF: a cover plus 33 illustrated lesson pages. A fixed-layout EPUB preserves the same visual pages and includes navigation. The lesson text is embedded in images and does not reflow; phones may require zooming.
A free opening-lesson PDF lets you preview the teaching style before buying.
WHO IT IS FOR
Readers who want a visual, example-led introduction or refresher. Follow the calculations and practice checks at your own pace.
Pramod Kumar 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 illustrated books use annotated diagrams, worked examples and practical scenarios to help readers build intuition for programming and the mathematics behind machine learning.
Explore his work at www.bitmonkey.ai.
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