Published Oct 13, 2017

[MINI] Big Oh Analysis

Delve into Big O analysis with Kyle Polich as he breaks down algorithm efficiency through engaging examples like card decks and grocery lists, demonstrating the power of understanding time complexity for everyday decision-making.

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Dexa/Kyle Polich

[MINI] Big Oh Analysis

Topics covered

Popular Clips

Dressing Dilemmas

The Halting Problem

Complex Card Algorithms

Shopping List Efficiency

Time Complexity Explained

Computational Theory

Efficient Shopping Algorithm

Understanding Big O

Episode Highlights

Big O Analysis

Searching Algorithms

Everyday Algorithm Examples

Related Episodes

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[MINI] Is the Internet Secure?

[MINI] Sudoku \in NP

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The Complexity of Learning Neural Networks

[MINI] Parallel Algorithms

The Computational Complexity of Machine Learning

First Order Logic

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[MINI] Primer on Deep Learning

[MINI] One Shot Learning

[MINI] Gradient Descent

[MINI] Sample Sizes

[MINI] Big Oh Analysis

Topics covered

Popular Clips

Episode Highlights

Big O AnalysisKyle Polich introduces Big O analysis, a key concept in computer science for assessing algorithm efficiency. He uses practical examples to explain how algorithms perform as input sizes increase, focusing on worst-case scenarios.

Big O Analysis

Searching AlgorithmsNext, Kyle Polich explores the intricacies of linear and binary search algorithms, highlighting their practical applications and efficiency. He uses relatable examples like card decks and grocery lists to illustrate these concepts.

Searching Algorithms

Everyday Algorithm ExamplesThe episode explores algorithmic principles applied to everyday problems, such as verifying a shopping list and selecting clothes, highlighting the importance of understanding time complexity.

Everyday Algorithm Examples

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