phase2 dl foundations
⬇ ダウンロードPHASE 2 Deep Learning Foundations Neurons, backpropagation, optimization, regularization, and PyTorch — from first prin- ciples Phase goal Understand neural networks from the ground up — forward pass, backpropagation, op
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00 overview
OVERVIEW Machine Learning & Deep Learning A depth-first curriculum, from math foundations to production Phase goal Turn “I can call .fit()” into “I understand, can implement, and can ship. ” This guide is the map for the
記事を読む →1 1 Regression
ˆy = β⊤x R2 ˆy = β⊤x R2 ˆy = β⊤x R2 ˆy = β⊤x R2 ˆy = β⊤x R2 ˆy = β⊤x R2 ˆy = β⊤x R2 ˆy = β⊤x R2 ˆy = β⊤x R2REGRESSION FROM BEGINNER TO EXPERT A complete, visual, application-driven course — from fitting a line through po
記事を読む →1 2 Bayes
P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H)BAYESIAN INFE
記事を読む →1 3 Tree Based Methods
xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? TREE-BASED METHODS FROM BEGINNER TO EXPERT A complete, visual, application-driven course — from a single decision tree through ran- dom forests and
記事を読む →1 4 Trees
xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? TREE-BASED METHODS FROM BEGINNER TO EXPERT A complete, visual, application-driven course — from a single decision tree through ran- dom forests and
記事を読む →1 6 Support Vector Machines
w⊤x + b w⊤x + b w⊤x + b w⊤x + b w⊤x + b w⊤x + b w⊤x + b w⊤x + b SUPPORT VECTOR MACHINES FROM BEGINNER TO EXPERT A complete, visual, application-driven course — from the maximal-margin idea through soft margins, the kerne
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