メインコンテンツへ移動

概念

144
フィルター

コア:24 · 生成:0 (非表示)

状態
説明できる不明点あり未開始

AI

5
難易度 3 · Connect復習中

Backpropagation

AIMLDeep Learning

Efficient computation of all gradients ∂L/∂w via the chain rule on the computational graph

Wiki →
難易度 3 · Connect復習中

Convolutional Neural Network

AIMLDeep Learning

Shared convolutional filters learn spatially local patterns; efficient for images via weight sharing

Wiki →
難易度 3 · Connect説明可能

Hierarchical Representation Learning

AIMLDeep Learning

Deep models learn hierarchical representations by composing many nonlinear layers

Wiki →
難易度 3 · Connect復習中

Neural Networks

AIMLDeep Learning

Compositions of linear layers + nonlinear activations: output = σ(W_L σ(…σ(W_1 x + b_1)…) + b_L)

Wiki →
難易度 3 · Connect復習中

Recurrent Neural Network

AIMLDeep Learning

h_t = σ(W_h h_{t-1} + W_x x_t + b): recurrent cell processes sequences step by step

Wiki →

CS

5
難易度 2 · Understand未開始

Algorithmic Complexity

CSAlgorithms

Algorithmic complexity predicts how runtime or memory grows as input size increases

Wiki →
難易度 3 · Connect説明可能

Dynamic Programming

CSAlgorithms

Solve overlapping subproblems once and cache results to avoid redundant computation

Wiki →
難易度 3 · Connect未開始

Graph Algorithms

CSAlgorithms

Algorithms operating on graphs G=(V,E): traversal, shortest paths, spanning trees, connectivity

Wiki →
難易度 2 · Understand説明可能

Greedy Algorithms

CSAlgorithms

Make the locally optimal choice at each step, achieving a global optimum when the greedy property holds

Wiki →
難易度 2 · Understand未開始

Sorting

CSAlgorithms

Arrange elements in order; comparison-based lower bound is Ω(n log n)

Wiki →

ML

5
難易度 2 · Understand説明可能

k-Nearest Neighbors

MLSupervised Learning

Classify by majority vote of k nearest neighbors; no training phase (lazy learner)

Wiki →
難易度 2 · Understand説明可能

Linear Regression

MLSupervised Learning

Fit a hyperplane y = Xβ + ε to minimize squared error ‖y − Xβ‖²

Wiki →
難易度 3 · Connect未開始

Logistic Regression

MLSupervised Learning

Binary classifier: P(y=1|x) = σ(w^T x + b), trained via cross-entropy loss

Wiki →
難易度 2 · Understand未開始

Supervised Learning

MLSupervised Learning

Learn a mapping f: X → Y from labeled training examples (x_i, y_i) pairs

Wiki →
難易度 3 · Connect未開始

Support Vector Machine

MLSupervised Learning

Find the maximum-margin hyperplane: maximize 2/‖w‖ s.t. y_i(w^T x_i + b) ≥ 1

Wiki →

Mathematics

5
難易度 3 · Connect未開始

Eigenvalues & Eigenvectors

MathematicsLinear Algebra

Av = λv: eigenvector v is only scaled (not rotated) by matrix A; λ is the eigenvalue

Wiki →
難易度 2 · Understand復習中

Matrix Multiplication

MathematicsLinear Algebra

C = AB where C_{ij} = Σ_k A_{ik}B_{kj}; represents composition of linear maps

Wiki →
難易度 4 · Apply未開始

Singular Value Decomposition

MathematicsLinear Algebra

A = UΣV^T: any matrix factors into rotation × scaling × rotation

Wiki →
難易度 2 · Understand未開始

Vector Space Basis

MathematicsLinear Algebra

A basis is a minimal set of independent vectors that spans a vector space

Wiki →
難易度 2 · Understand未開始

Vector Spaces

MathematicsLinear Algebra

A set closed under vector addition and scalar multiplication (satisfying 8 axioms)

Wiki →

Semiconductor

4
難易度 2 · Understand未開始

BJT Operation

Semiconductor

Current-controlled bipolar transistor: base current controls larger collector current via minority carrier injection

Wiki →
難易度 2 · Understand説明可能

CMOS Logic Gates

Semiconductor

Complementary NMOS+PMOS pairs form logic gates with near-zero static power and full-swing output

Wiki →
難易度 2 · Understand未開始

MOSFET Operation

Semiconductor

Voltage-controlled transistor: gate voltage modulates channel conductance between drain and source

Wiki →
難易度 1 · Memorize未開始

PN Junction

Semiconductor

Junction between p-type and n-type semiconductor forms depletion region and rectifying diode behavior

Wiki →