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Visual Maths for ML: Linear Algebra
See every calculation.
This book is 100% completeLast updated on 2026-10-19
See every calculation. Annotated diagrams and worked examples. Includes PDF and fixed-layout EPUB.
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About
About the Book
See every calculation. Build intuition through annotated diagrams, small worked examples and step-by-step explanations.
WHAT YOU WILL LEARN
1. Vectors and geometry
Vector components, addition, scaling, lengths, unit directions, direction cosines, dot products and orthogonality.
2. Matrices and model calculations
Matrix shapes, transposes, matrix multiplication, batches of examples, linear layers and bias terms.
3. Systems and structure
Span, linear independence, rank, linear systems, augmented matrices, inverses and determinants.
4. Fit and project
Vector projections, least squares and regression; interpreting the geometry of fitting a model.
5. Eigenvalues and transformations
Eigenvectors, eigenvalues, diagonalization, cases where diagonalization fails, rotations and orthogonal matrices.
6. Singular value decomposition
SVD as simple transformations, its connection to eigenpairs of A-transpose-A, worked decompositions and truncated-rank compression.
7. Principal component analysis
Center training data, compute covariance and variance, choose principal directions, calculate component scores and reconstruct approximations; fit on training data only.
8. Connect to neural models
Embeddings, context, exponentials, softmax, attention and neural layers, with worked calculations and practice checks.
WHAT IS INCLUDED
53-page PDF: a cover plus 52 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 Kumar 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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