算法

算法课程将帮助您学习解题计算过程,并且在软件中实现计算过程。您将学习设计搜索、排序和优化等算法,并且使用算法解决实际问题。

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筛选依据:
23 个结果
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Deep Learning

Deep Learning

deeplearning.ai
专项课程
评分为 4.8(满分 5 星)。
Natural Language Processing

Natural Language Processing

deeplearning.ai
专项课程
评分为 4.7(满分 5 星)。
AI Foundations for Everyone

AI Foundations for Everyone

IBM
专项课程
评分为 4.7(满分 5 星)。
Algorithms

Algorithms

Stanford University
专项课程
评分为 4.8(满分 5 星)。
Reinforcement Learning

Reinforcement Learning

University of Alberta
专项课程
评分为 4.7(满分 5 星)。
Accelerated Computer Science Fundamentals

Accelerated Computer Science Fundamentals

University of Illinois at Urbana-Champaign
专项课程
评分为 4.7(满分 5 星)。
数据结构与算法

数据结构与算法

University of California San Diego
专项课程
评分为 4.6(满分 5 星)。
Excel/VBA for Creative Problem Solving

Excel/VBA for Creative Problem Solving

University of Colorado Boulder
专项课程
评分为 4.9(满分 5 星)。
Introduction to Discrete Mathematics for Computer Science

Introduction to Discrete Mathematics for Computer Science

National Research University Higher School of Economics
专项课程
评分为 4.5(满分 5 星)。
Introduction to Programming in C

Introduction to Programming in C

Duke University
专项课程
评分为 4.5(满分 5 星)。
Bioinformatics

Bioinformatics

University of California San Diego
专项课程
评分为 4.5(满分 5 星)。
Probabilistic Graphical Models

Probabilistic Graphical Models

Stanford University
专项课程
评分为 4.6(满分 5 星)。
Algorithms for Battery Management Systems

Algorithms for Battery Management Systems

University of Colorado System
专项课程
评分为 4.8(满分 5 星)。
Fundamentals of Computing

Fundamentals of Computing

Rice University
专项课程
评分为 4.8(满分 5 星)。
Coding for Everyone: C and C++

Coding for Everyone: C and C++

University of California, Santa Cruz
专项课程
评分为 4.5(满分 5 星)。
Machine Learning and Reinforcement Learning in Finance

Machine Learning and Reinforcement Learning in Finance

New York University
专项课程
评分为 3.7(满分 5 星)。

    关于 算法 的常见问题

  • 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!