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    • Reinforcement Learning

    필터링 기준

    "reinforcement learning"에 대한 53개의 결과

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      University of Alberta

      Reinforcement Learning

      획득할 기술: Approximation, Artificial Neural Networks, Business Psychology, Computer Programming, Deep Learning, Entrepreneurship, Euler'S Totient Function, Leadership and Management, Machine Learning, Machine Learning Algorithms, Markov Model, Mathematics, Operations Research, Planning, Process, Python Programming, Reinforcement Learning, Research and Design, Statistical Programming, Strategy and Operations, Supply Chain and Logistics

      4.7

      (2.9k개의 검토)

      Intermediate · Specialization · 3-6 Months

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      DeepLearning.AI

      Deep Learning

      획득할 기술: Advertising, Algorithms, Analysis, Applied Machine Learning, Artificial Neural Networks, Bayesian Statistics, Big Data, Business Psychology, Communication, Computational Logic, Computer Architecture, Computer Graphic Techniques, Computer Graphics, Computer Networking, Computer Programming, Computer Vision, Data Management, Decision Making, Deep Learning, Entrepreneurship, General Statistics, Hardware Design, Human Computer Interaction, Interactive Design, Leadership and Management, Linear Algebra, Machine Learning, Machine Learning Algorithms, Marketing, Markov Model, Mathematical Theory & Analysis, Mathematics, Modeling, Natural Language Processing, Network Architecture, Network Model, Probability & Statistics, Project Management, Python Programming, Regression, Sales, Statistical Machine Learning, Statistical Programming, Strategy, Strategy and Operations, Supply Chain, Supply Chain Systems, Supply Chain and Logistics, Tensorflow, Theoretical Computer Science, User Experience

      4.8

      (134.2k개의 검토)

      Intermediate · Specialization · 3-6 Months

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      University of Alberta

      Fundamentals of Reinforcement Learning

      획득할 기술: Mathematics, Research and Design, Computer Programming, Strategy and Operations, Machine Learning, Statistical Programming, Python Programming, Process, Operations Research, Reinforcement Learning

      4.8

      (2.4k개의 검토)

      Intermediate · Course · 1-3 Months

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      New York University

      Machine Learning and Reinforcement Learning in Finance

      획득할 기술: Accounting, Algorithms, Applied Machine Learning, Applied Mathematics, Artificial Neural Networks, Business Analysis, Calculus, Computer Programming, Data Analysis, Deep Learning, Entrepreneurship, Finance, Financial Analysis, General Statistics, Investment Management, Machine Learning, Machine Learning Algorithms, Mathematics, Probability & Statistics, Python Programming, Reinforcement Learning, Tensorflow, Theoretical Computer Science

      3.7

      (747개의 검토)

      Intermediate · Specialization · 3-6 Months

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      Google Cloud

      Reinforcement Learning: Qwik Start

      Beginner · Project · Less Than 2 Hours

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      IBM Skills Network

      Deep Learning and Reinforcement Learning

      획득할 기술: Computer Programming, Python Programming, Machine Learning, Computer Vision, Reinforcement Learning, Deep Learning, Statistical Programming, Artificial Neural Networks

      4.6

      (104개의 검토)

      Intermediate · Course · 1-3 Months

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      DeepLearning.AI, Stanford University

      Machine Learning

      획득할 기술: Accounting, Algorithms, Applied Machine Learning, Artificial Neural Networks, Calculus, Communication, Computer Programming, Computer Vision, Cost, Data Analysis, Data Management, Data Mining, Data Structures, Deep Learning, Econometrics, Feature Engineering, General Statistics, Linear Algebra, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Operations Research, Probability & Statistics, Probability Distribution, Python Programming, Regression, Reinforcement Learning, Research and Design, Statistical Classification, Statistical Machine Learning, Statistical Programming, Strategy and Operations, Tensorflow, Theoretical Computer Science

      4.9

      (2.2k개의 검토)

      Beginner · Specialization · 1-3 Months

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      DeepLearning.AI

      Unsupervised Learning, Recommenders, Reinforcement Learning

      획득할 기술: Algorithms, General Statistics, Research and Design, Computer Programming, Mathematical Theory & Analysis, Theoretical Computer Science, Tensorflow, Machine Learning, Python Programming, Data Mining, Mathematics, Probability & Statistics, Statistical Programming, Communication, Machine Learning Algorithms, Applied Machine Learning, Artificial Neural Networks, Data Analysis, Operations Research, Strategy and Operations, Probability Distribution, Reinforcement Learning

      5.0

      (83개의 검토)

      Beginner · Course · 1-4 Weeks

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      Google Cloud

      Machine Learning for Trading

      획득할 기술: Artificial Neural Networks, Business Psychology, Cloud Computing, Cloud Platforms, Computer Programming, Entrepreneurship, Finance, General Statistics, Investment Management, Leadership and Management, Machine Learning, Marketing, Mathematics, Modeling, Probability & Statistics, Python Programming, Reinforcement Learning, Risk Management, Sales, Statistical Programming, Strategy, Strategy and Operations, Trading

      3.9

      (947개의 검토)

      Intermediate · Specialization · 1-3 Months

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      University of Pennsylvania

      AI For Business

      획득할 기술: Accounting, Applied Machine Learning, Artificial Neural Networks, Big Data, BlockChain, Business Analysis, Clinical Data Management, Computational Thinking, Computer Programming, Customer Analysis, Customer Relationship Management, Customer Success, Data Analysis, Data Management, Data Mining, Data Warehousing, Database Administration, Databases, Deep Learning, Entrepreneurship, Feature Engineering, Finance, Financial Analysis, Human Resources, Leadership, Leadership and Management, Machine Learning, Marketing, Natural Language Processing, Reinforcement Learning, Sales, Security Engineering, Software Security, Strategy and Operations, Theoretical Computer Science

      4.6

      (89개의 검토)

      Beginner · Specialization · 3-6 Months

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      IBM Skills Network

      IBM Machine Learning

      획득할 기술: Algebra, Algorithms, Analysis, Applied Machine Learning, Artificial Neural Networks, Bayesian Statistics, Business Analysis, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Data Analysis, Data Management, Data Structures, Data Visualization, Databases, Deep Learning, Dimensionality Reduction, Experiment, Exploratory Data Analysis, Feature Engineering, Forecasting, General Statistics, Linear Algebra, Machine Learning, Machine Learning Algorithms, Mathematics, NoSQL, Probability & Statistics, Python Programming, Regression, Reinforcement Learning, Statistical Machine Learning, Statistical Programming, Statistical Visualization, Theoretical Computer Science

      4.6

      (1k개의 검토)

      Intermediate · Professional Certificate · 3-6 Months

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      University of Alberta

      A Complete Reinforcement Learning System (Capstone)

      획득할 기술: Artificial Neural Networks, Machine Learning, Reinforcement Learning

      4.7

      (562개의 검토)

      Intermediate · Course · 1-3 Months

    reinforcement learning과(와) 관련된 검색

    reinforcement learning: qwik start
    reinforcement learning in finance
    reinforcement learning for trading strategies
    fundamentals of reinforcement learning
    a complete reinforcement learning system (capstone)
    unsupervised learning, recommenders, reinforcement learning
    deep learning and reinforcement learning
    machine learning and reinforcement learning in finance
    12345

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

    • Reinforcement Learning: University of Alberta
    • Deep Learning: DeepLearning.AI
    • Fundamentals of Reinforcement Learning: University of Alberta
    • Machine Learning and Reinforcement Learning in Finance: New York University
    • Reinforcement Learning: Qwik Start: Google Cloud
    • Deep Learning and Reinforcement Learning: IBM Skills Network
    • Machine Learning: DeepLearning.AI
    • Unsupervised Learning, Recommenders, Reinforcement Learning: DeepLearning.AI
    • Machine Learning for Trading: Google Cloud
    • AI For Business: University of Pennsylvania

    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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