알고리즘

알고리즘 강좌는 문제 해결을 위한 과정을 명확하게 하고 소프트웨어 내의 처리를 효과적이게 구현하는 능력을 발달시켜줍니다. 검색, 정렬 및 최적화를 위한 알고리즘 디자인을 배우고 연습 질문에 답하는데 이를 적용할 수 있습니다.

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필터링 기준:
22 결과
정렬 기준:
Deep Learning

Deep Learning

deeplearning.ai
특화 과정
5점 만점에 4.8점을 받았습니다.
Natural Language Processing

Natural Language Processing

deeplearning.ai
특화 과정
5점 만점에 4.7점을 받았습니다.
AI Foundations for Everyone

AI Foundations for Everyone

IBM
특화 과정
5점 만점에 4.7점을 받았습니다.
Algorithms

Algorithms

Stanford University
특화 과정
5점 만점에 4.8점을 받았습니다.
Accelerated Computer Science Fundamentals

Accelerated Computer Science Fundamentals

University of Illinois at Urbana-Champaign
특화 과정
5점 만점에 4.7점을 받았습니다.
Reinforcement Learning

Reinforcement Learning

University of Alberta
특화 과정
5점 만점에 4.7점을 받았습니다.
Data Structures and Algorithms

Data Structures and Algorithms

University of California San Diego
특화 과정
5점 만점에 4.6점을 받았습니다.
Introduction to Discrete Mathematics for Computer Science

Introduction to Discrete Mathematics for Computer Science

National Research University Higher School of Economics
특화 과정
5점 만점에 4.5점을 받았습니다.
Excel/VBA for Creative Problem Solving

Excel/VBA for Creative Problem Solving

University of Colorado Boulder
특화 과정
5점 만점에 4.9점을 받았습니다.
Introduction to Programming in C

Introduction to Programming in C

Duke University
특화 과정
5점 만점에 4.5점을 받았습니다.
Bioinformatics

Bioinformatics

University of California San Diego
특화 과정
5점 만점에 4.5점을 받았습니다.
Probabilistic Graphical Models

Probabilistic Graphical Models

Stanford University
특화 과정
5점 만점에 4.6점을 받았습니다.
Fundamentals of Computing

Fundamentals of Computing

Rice University
특화 과정
5점 만점에 4.8점을 받았습니다.
Coding for Everyone: C and C++

Coding for Everyone: C and C++

University of California, Santa Cruz
특화 과정
5점 만점에 4.5점을 받았습니다.
Algorithms for Battery Management Systems

Algorithms for Battery Management Systems

University of Colorado System
특화 과정
5점 만점에 4.8점을 받았습니다.
程序设计与算法

程序设计与算法

Peking University
특화 과정
5점 만점에 4.4점을 받았습니다.

    알고리즘에 대한 자주 묻는 질문

  • An algorithm is a step-by-step process used to solve a problem or reach a desired goal. It's a simple concept; you use your own algorithms for everyday tasks like deciding whether to drive or take the subway to work, or determining what you need from the grocery store. Software programs are an example of much more powerful algorithms, with computing resources used to execute multiple complex algorithms in parallel to solve much higher-level problems.

    As computers become more and more powerful, algorithms are helping them take on a life of their own - literally! Machine learning techniques rely on algorithms that learn and improve over time without need for a programmer's guidance. These techniques can be used to train algorithms for relatively simple tasks like image recognition or the automation and optimization of business workflows. And at their most complex, these algorithms are at the core of building the deep learning and artificial intelligence capabilities that many experts expect will transform our world even more than the advent of the internet!