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    • Reinforcement Learning
    Related topics:深層強化学習カルマンフィルター上級Pythonアルゴリズム的思考アルゴリズム取引IBM

    フィルター

    「reinforcement learning」の47件の結果

    • University of Alberta

      University of Alberta

      Reinforcement Learning

      習得できるスキル: Artificial Neural Networks, Entrepreneurship, Euler'S Totient Function, Leadership and Management, Machine Learning, Machine Learning Algorithms, Markov Model, Mathematics, Operations Research, Planning, Reinforcement Learning, Research and Design, Strategy and Operations, Supply Chain and Logistics, Theoretical Computer Science

      4.7

      (2.9k件のレビュー)

      Intermediate · Specialization · 3+ Months

    • University of Alberta

      University of Alberta

      Fundamentals of Reinforcement Learning

      習得できるスキル: Process, Mathematics, Strategy and Operations, Research and Design, Operations Research, Machine Learning, Reinforcement, Reinforcement Learning

      4.8

      (2.3k件のレビュー)

      Intermediate · Course · 1-3 Months

    • New York University

      New York University

      Machine Learning and Reinforcement Learning in Finance

      習得できるスキル: Applied Mathematics, Calculus, Computer Programming, Finance, General Statistics, Investment Management, Machine Learning, Machine Learning Algorithms, Markov Model, Mathematics, Probability & Statistics, Python Programming, Reinforcement Learning, Statistical Programming, Theoretical Computer Science

      3.7

      (741件のレビュー)

      Intermediate · Specialization · 3+ Months

    • New York Institute of Finance

      New York Institute of Finance

      Machine Learning for Trading

      習得できるスキル: Accounting, Artificial Neural Networks, Business Analysis, Cloud Computing, Computer Programming, Data Analysis, Finance, Financial Analysis, General Statistics, Investment Management, Machine Learning, Mathematics, Probability & Statistics, Python Programming, Reinforcement Learning, Statistical Programming, Trading

      3.9

      (930件のレビュー)

      Intermediate · Specialization · 1-3 Months

    • IBM

      IBM

      Deep Learning and Reinforcement Learning

      習得できるスキル: Machine Learning, Artificial Neural Networks, Python Programming, Computer Programming, Statistical Programming, Deep Learning, Computer Vision, Reinforcement Learning

      4.6

      (95件のレビュー)

      Intermediate · Course · 1-3 Months

    • New York University

      New York University

      Reinforcement Learning in Finance

      習得できるスキル: Machine Learning

      3.6

      (118件のレビュー)

      Advanced · Course · 1-4 Weeks

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      DeepLearning.AI

      DeepLearning.AI

      Deep Learning

      習得できるスキル: Algorithms, Applied Machine Learning, Artificial Neural Networks, Bayesian Statistics, Big Data, Communication, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Convolutional Neural Network, Data Management, Deep Learning, Entrepreneurship, General Statistics, Human Computer Interaction, Interactive Design, Linear Algebra, Machine Learning, Machine Learning Algorithms, Mathematical Optimization, Mathematical Theory & Analysis, Mathematics, Natural Language Processing, Probability & Statistics, Python Programming, Regression, Statistical Machine Learning, Statistical Programming, Strategy and Operations, Theoretical Computer Science

      4.8

      (132.4k件のレビュー)

      Intermediate · Specialization · 3+ Months

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      University of Pennsylvania

      University of Pennsylvania

      AI For Business

      習得できるスキル: Applied Machine Learning, Big Data, Computational Thinking, Computer Programming, Data Management, Database Administration, Databases, Entrepreneurship, Finance, Human Resources, Leadership, Leadership and Management, Machine Learning, Marketing, People Management, Reinforcement Learning, Security Engineering, Software Security, Strategy and Operations, Theoretical Computer Science

      4.6

      (68件のレビュー)

      Beginner · Specialization · 3+ Months

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      Google Cloud

      Google Cloud

      Reinforcement Learning: Qwik Start

      Beginner · Project · Less Than 2 Hours

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      無料

      University of Washington

      University of Washington

      Computational Neuroscience

      習得できるスキル: Python Programming, Data Analysis Software, Artificial Neural Networks, Neuroscience, Mathematics, Probability & Statistics, Other Programming Languages, Statistical Programming, Modeling, Machine Learning, Data Analysis, Communication, Computer Programming, Marketing, Reinforcement Learning, Deep Learning, Machine Learning Algorithms, Linear Algebra

      4.6

      (927件のレビュー)

      Beginner · Course · 1-3 Months

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      University of Alberta

      University of Alberta

      A Complete Reinforcement Learning System (Capstone)

      習得できるスキル: Artificial Neural Networks, Reinforcement Learning, Machine Learning, Reinforcement

      4.7

      (549件のレビュー)

      Intermediate · Course · 1-3 Months

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      CFA Institute

      CFA Institute

      Machine Learning for Investment Professionals

      習得できるスキル: Machine Learning, Modeling, Communication, Marketing, Deep Learning

      Beginner · Course · 1-3 Months

    reinforcement learningに関連する検索

    reinforcement learning in finance
    reinforcement learning for trading strategies
    reinforcement learning: qwik start
    fundamentals of reinforcement learning
    a complete reinforcement learning system (capstone)
    deep learning and reinforcement learning
    machine learning and reinforcement learning in finance
    overview of advanced methods of reinforcement learning in finance
    1234

    要約して、reinforcement learning の人気コース10選をご紹介します。

    • Reinforcement Learning: University of Alberta
    • Fundamentals of Reinforcement Learning: University of Alberta
    • Machine Learning and Reinforcement Learning in Finance: New York University
    • Machine Learning for Trading: New York Institute of Finance
    • Deep Learning and Reinforcement Learning: IBM
    • Reinforcement Learning in Finance: New York University
    • Deep Learning: DeepLearning.AI
    • AI For Business: University of Pennsylvania
    • Reinforcement Learning: Qwik Start: Google Cloud
    • Computational Neuroscience: University of Washington

    Machine Learningで学べるスキル

    Pythonプログラミング (33)
    TensorFlow (32)
    ディープラーニング (30)
    人工ニューラルネットワーク (24)
    ビッグデータ (18)
    統計的分類 (17)
    代数 (10)
    ベイズ (10)
    線型代数学 (10)
    線形回帰 (9)
    NumPy (9)

    強化学習に関するよくある質問

    • 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.‎

    このFAQの内容は、情報提供のみを目的としています。受講生は、自分の個人的、職業的、経済的な目標に合ったコースやその他の資格を取得するために、さらに調べることをお勧めします。
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