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3.7 Indep endence and Conditional Indep endence 3.9 Common Probabilit Probability y Distributions. 3.7 Independence endenceProbabilit and Conditional Independence 3.6 Exp The Chain Rule of Conditional Probabilities 3.8 Expectation, ectation, Variance and Co Cov variance. 3.6 The Chain Rule of Conditional 3.5 Indep Conditional y. 3.4 Marginal Probabilit Probability 3.3 Conditional Probability Distributions 3.5 Probabilit Probability y.

3.3 Probabilit Probability y Distributions. 3 Probability and Information Theory 3.1 Why Probability?. 3 Probabilit Probability y and Information Theory 3.1 Wh Why y Probabilit Probability? y?. 2.12 Example: Principal Components Analysis. 2.12 Example: Principal Comp Components onents 2.11 The Determinant. 2.9 The The Determinan Mo ore-Penrose 2.11 Determinant t. 2.9 Mo Moore-P ore-P ore-Penrose enrose Pseudoinverse 2.8 The Singular Value Decomp. 2.7 Eigendecomposition 2.6 Sp ecial Kinds Matrices and V 2.8 Singular ValueofDecomp Decomposition osition. 2.6 Special ecial Dep Kinds of Matrices and V. 2.2 Linear Multiplying Matrices and Vectors 2.4 Dep Dependence endence and Span. 2.1 Scalars, ectors, Matrices and T 2.3 Iden Identit tit tity yV and In Inverse verse Matrices. 2 2.2 LinearMultiplying Algebra Matrices and Vectors. I Applied Math and Mac Machine hine Learning Basics I Applied Math and Machine Learning Basics 2 Linear Algebra 2.1 Scalars, Vectors, Matrices and Tensors. 1 1.2 Introduction Historical Trends in Deep Learning. Notation 1 In Intro tro troduction duction 1.1 Who Should Read This Bo Book? ok?. Wcebsite A kno knowledgmen wledgmen wledgments ts Deep Learning Deep Learning Ian Go Goo odfello dfellow w Yosh oshua ua Bengio Ian GoCourville odfellow Aaron Yoshua Bengio Aaron Courville

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