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मुख्य: 24 · निर्मित: 0 (छिपे हुए)

स्थिति
समझा सकता/सकती हूँअस्पष्टशुरू नहीं किया

AI

5
कठिनाई 3 · Connectसमीक्षा जारी

Backpropagation

AIMLDeep Learning

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

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Convolutional Neural Network

AIMLDeep Learning

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

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Hierarchical Representation Learning

AIMLDeep Learning

Deep models learn hierarchical representations by composing many nonlinear layers

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Neural Networks

AIMLDeep Learning

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

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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

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CS

5
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Algorithmic Complexity

CSAlgorithms

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

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Dynamic Programming

CSAlgorithms

Solve overlapping subproblems once and cache results to avoid redundant computation

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Graph Algorithms

CSAlgorithms

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

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Greedy Algorithms

CSAlgorithms

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

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Sorting

CSAlgorithms

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

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ML

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k-Nearest Neighbors

MLSupervised Learning

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

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Linear Regression

MLSupervised Learning

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

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Logistic Regression

MLSupervised Learning

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

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Supervised Learning

MLSupervised Learning

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

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Support Vector Machine

MLSupervised Learning

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

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Mathematics

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Eigenvalues & Eigenvectors

MathematicsLinear Algebra

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

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Matrix Multiplication

MathematicsLinear Algebra

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

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Singular Value Decomposition

MathematicsLinear Algebra

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

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Vector Space Basis

MathematicsLinear Algebra

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

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Vector Spaces

MathematicsLinear Algebra

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

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Semiconductor

4
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BJT Operation

Semiconductor

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

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CMOS Logic Gates

Semiconductor

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

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MOSFET Operation

Semiconductor

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

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PN Junction

Semiconductor

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

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