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    • Reinforcement Learning

    필터링 기준

    "reinforcement learning"에 대한 84개의 결과

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      Google Cloud

      Recommendation Systems on Google Cloud

      획득할 기술: Probability & Statistics, Machine Learning, General Statistics, Business Psychology, Entrepreneurship, Cloud Computing, Google Cloud Platform, Reinforcement Learning, Applied Machine Learning, Tensorflow

      4.5

      (458개의 검토)

      Advanced · Course · 1-3 Months

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      Coursera Project Network

      Kubernetes: Create Multi-App Cluster with Ingress & Logging

      획득할 기술: Applied Machine Learning, Cloud Computing, Computer Vision, Kubernetes, Machine Learning, Reinforcement Learning

      4.6

      (28개의 검토)

      Intermediate · Guided Project · Less Than 2 Hours

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      Korea Advanced Institute of Science and Technology(KAIST)

      Big data and Language 1

      획득할 기술: Big Data, Data Management, Applied Machine Learning, Communication, Machine Learning, Reinforcement Learning

      4.5

      (111개의 검토)

      Beginner · Course · 1-4 Weeks

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      University of Colorado Boulder

      Introduction to Deep Learning

      획득할 기술: Deep Learning, Machine Learning, Artificial Neural Networks, Applied Machine Learning, Machine Learning Algorithms, Reinforcement Learning

      3.3

      (6개의 검토)

      Intermediate · Course · 1-3 Months

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      Google Cloud

      Virtual Agent Development in Dialogflow CX for Citizen Devs

      획득할 기술: Applied Machine Learning, Machine Learning, Reinforcement Learning, Natural Language Processing, Software Architecture, Software Engineering, Theoretical Computer Science

      4.6

      (19개의 검토)

      Intermediate · Course · 1-3 Months

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      University of Cape Town

      How Entrepreneurs in Emerging Markets can master the Blockchain Technology

      획득할 기술: BlockChain, Finance, Cryptography, Machine Learning, Security Engineering, Theoretical Computer Science, Machine Learning Algorithms, Reinforcement Learning

      4.6

      (39개의 검토)

      Beginner · Course · 1-4 Weeks

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      Politecnico di Milano

      Machine Learning: an overview

      획득할 기술: Machine Learning, Machine Learning Algorithms, Reinforcement Learning

      4.5

      (21개의 검토)

      Beginner · Course · 1-4 Weeks

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      무료

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      Hebrew University of Jerusalem

      Israel State and Society

      획득할 기술: Business Psychology, Culture, Leadership and Management, Entrepreneurship, Adaptability, Applied Machine Learning, Econometrics, General Statistics, Machine Learning, Market Research, Probability & Statistics, Reinforcement Learning, Research and Design

      4.7

      (331개의 검토)

      Beginner · Course · 3-6 Months

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      Alfaisal University | KLD

      نماذج تقييم الأداء | Performance Management Models

      획득할 기술: Leadership and Management, Performance Management, Strategy and Operations, Accounting, Application Development, Applied Machine Learning, Entrepreneurship, FinTech, Finance, Machine Learning, Planning, Reinforcement Learning, Software Architecture, Software Engineering, Supply Chain and Logistics, Theoretical Computer Science

      4.8

      (16개의 검토)

      Beginner · Course · 1-4 Weeks

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      무료

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      Universidad de los Andes

      Introducción a la inteligencia artificial contemporánea

      획득할 기술: Machine Learning, Machine Learning Algorithms, Natural Language Processing, Applied Machine Learning, Business Psychology, Computational Thinking, Computer Programming, Computer Vision, Culture, Decision Making, Entrepreneurship, Human Computer Interaction, Human Resources, Leadership and Management, Reinforcement Learning, Theoretical Computer Science, User Experience

      4.6

      (13개의 검토)

      Beginner · Course · 1-3 Months

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      Google Cloud

      Innovating with Data and Google Cloud 日本語版

      획득할 기술: Applied Machine Learning, Computer Networking, Databases, Machine Learning, Network Security, Reinforcement Learning, Security Engineering

      3.9

      (7개의 검토)

      Beginner · Course · 1-3 Months

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      Coursera Project Network

      Apprendre à une IA des jeux de stratégie avec easyAI

      획득할 기술: Machine Learning, Reinforcement Learning

      Advanced · Guided Project · Less Than 2 Hours

    reinforcement learning과(와) 관련된 검색

    reinforcement learning in finance
    reinforcement learning for trading strategies
    reinforcement learning: qwik start
    a complete reinforcement learning system (capstone)
    fundamentals of reinforcement learning
    unsupervised learning, recommenders, reinforcement learning
    machine learning and reinforcement learning in finance
    deep learning and reinforcement learning
    1…456…7

    요약하자면, 여기에 가장 인기 있는 reinforcement learning 강좌 10개가 있습니다.

    • Recommendation Systems on Google Cloud: Google Cloud
    • Kubernetes: Create Multi-App Cluster with Ingress & Logging: Coursera Project Network
    • Big data and Language 1: Korea Advanced Institute of Science and Technology(KAIST)
    • Introduction to Deep Learning: University of Colorado Boulder
    • Virtual Agent Development in Dialogflow CX for Citizen Devs: Google Cloud
    • How Entrepreneurs in Emerging Markets can master the Blockchain Technology: University of Cape Town
    • Machine Learning: an overview: Politecnico di Milano
    • Israel State and Society: Hebrew University of Jerusalem
    • نماذج تقييم الأداء | Performance Management Models: Alfaisal University | KLD
    • Introducción a la inteligencia artificial contemporánea: Universidad de los Andes

    Machine Learning에서 학습할 수 있는 스킬

    Python 프로그래밍 (33)
    TensorFlow (32)
    심층 학습 (30)
    인공 신경 회로망 (24)
    빅 데이터 (18)
    통계 분류 (17)
    강화 학습 (13)
    대수학 (10)
    베이지안 (10)
    선형 대수 (10)
    선형 회귀 (9)
    Numpy (9)

    강화 학습에 대한 자주 묻는 질문

    • Reinforcement learning is a machine learning paradigm in which software agents use a process of trial and error to learn how to complete tasks in a way that maximizes cumulative rewards as defined by their programmers. In contrast to supervised learning paradigms, reinforcement learning systems do not need labeled input/output pairs or explicit corrections of suboptimal actions; and, in contrast to unsupervised learning, reinforcement learning defines an explicit goal, which is the maximization of the value returned by the Q-learning (or “quality” learning) algorithm as a result of its actions.

      Because it combines the goal orientation of supervised learning with the flexibility of unsupervised learning, reinforcement learning is very important in creating artificial intelligence (AI) applications requiring successful problem-solving in complex situations. For example, they are often used in financial engineering to develop optimal trading algorithms for the stock market. They are also used to build intelligent systems to allow robots and self-driving cars to navigate real-world environments safely.‎

    • As one of the main paradigms for machine learning, reinforcement learning is an essential skill for careers in this fast-growing field. Reinforcement learning is particularly important for developing artificially intelligent digital agents for real-world problem-solving in industries like finance, automotive, robotics, logistics, and smart assistants. According to Glassdoor, the average annual salary for machine learning engineers in America is $114,121 per year, a high level of pay which reflects the high level of demand for this expertise.‎

    • Absolutely. Coursera hosts a wide variety of courses in reinforcement learning and related topics in machine learning, as well as the use of these techniques in applied contexts such as finance and self-driving cars. These courses and Specializations are offered by top-ranked institutions in this field, including the deepmind.ai, New York University, the University of Toronto, and the University of Alberta’s Machine Intelligence Institute. You can learn remotely on a flexible schedule while still getting feedback from expert professors and instructors, ensuring that you’ll get a high quality education with all the reinforcement you need to learn these valuable skills with confidence.‎

    • Because reinforcement learning itself isn't a beginner-level subject, you'll need to have a good grasp on the fundamentals of machine learning before starting to learn it. Additionally, many courses will require you to have a strong background in high-level mathematics such as linear algebra, statistics, and probability. Most courses will require you to be proficient in Python, although people familiar with other programming languages like C++, Matlab, and JavaScript can often use those skills to help them learn reinforcement learning. Having the ability to implement algorithms from pseudocode may be another prerequisite. As you progress, you'll gain skills in using reinforcement learning solutions to solve problems with probabilistic artificial intelligence, function approximation, and intelligent systems.‎

    • People best suited to roles within the reinforcement learning realm should have a passion for machine learning with a drive for analytics and data and an interest in providing frontline support to solve real-world problems while leveraging innate creative problem-solving skills. Additionally, many companies like to see that candidates have strong communication skills and the ability to collaborate across disciplines and departments. There are a variety of roles associated with reinforcement learning, including analysts, engineers, and researchers. In late February 2021, there were more than 1,800 job listings for people proficient in reinforcement learning on LinkedIn.‎

    • If you want to be a part of the future of machine learning, learning reinforcement learning may be a good move for you. This innovative machine learning technique creates an algorithm that learns through trial and error, leading to a combination of short- and long-term rewards such as the ability to define sequences to solve problems using a reward-based learning approach. It's useful across multiple industries, including the tech industry, business, advertising, finance, and e-commerce, all of which find reinforcement learning useful in part because of its ability to offer greater personalization. Ultimately, if you want to work within AI and machine learning, this could be a step to advancing your goals.‎

    이 FAQ 콘텐츠는 정보 전달 목적만으로 사용할 수 있습니다. 학습자는 과정 및 기타 학점 정보가 개인적, 직업적 및 재정적 목표에 부합하는지 확인하기 위해 추가 조사를 수행하는 것이 좋습니다.
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