Computational complexity theory

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Related lectures (30)

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Algorithmic Complexity: Visualization and Analysis

Explores algorithmic complexity, visualization of functions, and algorithm efficiency analysis using Python.

P vs NP: Complexity Theory

Delves into complexity theory, focusing on the P vs NP problem and the classification of computational problems based on efficiency.

Generalization in Deep Learning

Explores generalization in deep learning, covering model complexity, implicit bias, and the double descent phenomenon.

Distinct Elements: Count and Hash Functions

Covers counting distinct elements using hash functions and the median trick.

Algorithmic Complexity: Theta Notation

Explores algorithmic complexity, comparing growth rates using Theta notation and characterizing different complexity classes.

Computation & Algorithms II: Sorting and Recursive Algorithms

Explores sorting and recursive algorithms, including complexity analysis, maximal value, and binary search.

Numerical Methods: Runge-Kutta Approximation

Covers the Runge-Kutta method for approximating solutions of differential equations.

Theory of Computation: Monotone Complexity and XOR-SAT Lower Bounds

Explores monotone complexity, XOR-SAT lower bounds, and their implications in computational theory.

Distributed Quantum Computing

Explores communication complexity and quantum computation models in Distributed Quantum Computing.

Implementation Research: Concepts and Scope

Explores Implementation Research, addressing challenges in implementing health interventions and developing effective strategies for infectious diseases of poverty.