Vizuara Books
Mathematical Foundations for Machine Learning
The linear algebra, probability, and calculus that make ML work.
See the math behind machine learning as geometry and code: matrices as transformations, determinants and eigenvectors, probability and distributions, and the calculus of gradient descent. Every idea is built by hand and drawn out, ending in a neural network trained from scratch.
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00Why Math for Machine Learning3 capsules
Why Math for Machine Learning — 3 chapters.
01The Math Under the Modelintuitionfree13 min02How to Read This Bookconcept🔒13 min03Vectors as Arrows and as Listsconcept🔒13 min01Linear Algebra as Transformations7 capsules
Linear Algebra as Transformations — 7 chapters.
04Linear Combinations and Spanconcept🔒12 min05The Dot Product, Two Waysmath🔒12 min06The Dot Product as a Linear Transformationdeep-dive🔒12 min07A Matrix Is a Transformationintuition🔒14 min08Matrix Multiplication as Compositionconcept🔒13 min09The Identity and the Inversemath🔒13 min10Transformations in 3Dconcept🔒14 min02Determinants and the Shape of Space5 capsules
Determinants and the Shape of Space — 5 chapters.
11What the Determinant Measuresintuition🔒13 min12Computing 2x2 and 3x3 Determinantsmath🔒13 min13Zero Determinant and Collapsedeep-dive🔒13 min14Eigenvalues and Eigenvectorsconcept🔒16 min15Eigen-Intuition and Why It Matters for MLintuition🔒13 min03Probability and Statistics Foundations5 capsules
Probability and Statistics Foundations — 5 chapters.
16Reasoning Under Uncertaintyconcept🔒13 min17Conditional Probability and Bayes' Rulemath🔒14 min18Random Variables and Expectationconcept🔒14 min19Variance, Standard Deviation, and Spreadmath🔒14 min20Covariance and Correlationdeep-dive🔒14 min04Probability Distributions5 capsules
Probability Distributions — 5 chapters.
21Distributions as Shapes of Chanceconcept🔒13 min22Bernoulli and Binomial Distributionsmath🔒13 min23The Normal Distributiondeep-dive🔒12 min24Likelihood and Maximum Likelihoodconcept🔒13 min25From Likelihood to Loss Functionsintuition🔒14 min05Python and Numerical Computing7 capsules
Python and Numerical Computing — 6 chapters.
26Python for Math: A Fast Startcode🔒13 min27Objects, Classes, and Building Blockscode🔒14 min28NumPy Arrays and Vectorizationcode🔒14 min29Linear Algebra in NumPycode🔒14 min30Pandas for Datacode🔒12 min31Visualizing Data and Distributionscode🔒13 min32Python for Machine Learningcode🔒14 min06Calculus and the Engine of Learning7 capsules
Calculus and the Engine of Learning — 7 chapters.
33Derivatives as Rates of Changeconcept🔒13 min34Integrals and the Area Under a Curvemath🔒14 min35Partial Derivatives and the Gradientconcept🔒13 min36The Chain Rule and Backpropagationdeep-dive🔒14 min37Gradient Descentconcept🔒15 min38Momentum and RMSPropmath🔒14 min39The Adam Optimizerdeep-dive🔒15 min07Putting It Together4 capsules
Putting It Together — 4 chapters.
40Deep Learning Libraries in Pythoncode🔒12 min41A Neuron from Scratchcode🔒12 min42A Neural Network from Scratchproject🔒15 min43The Math You Now Ownintuition🔒13 minRatings & reviews
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