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Reinforcement Learning 课程

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“reinforcement learning”共返回 64 条结果

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    Reinforcement Learning
    University of Alberta
    专项课程
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    评分为 4.7(满分 5 星)。2186 条评论
    4.7(2,186)
    48k 名学生
    Intermediate LevelIntermediate
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    Fundamentals of Reinforcement Learning
    University of Alberta
    课程
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    评分为 4.8(满分 5 星)。1822 条评论
    4.8(1,822)
    45k 名学生
    Intermediate LevelIntermediate
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    Deep Learning
    DeepLearning.AI
    专项课程
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    评分为 4.8(满分 5 星)。118803 条评论
    4.8(118,803)
    1 分钟 名学生
    Intermediate LevelIntermediate
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    Machine Learning and Reinforcement Learning in Finance
    New York University
    专项课程
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    评分为 3.7(满分 5 星)。669 条评论
    3.7(669)
    35k 名学生
    Intermediate LevelIntermediate
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    Introduction to Reinforcement Learning in Python
    Coursera Project Network

    新

    指导项目
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    评分为 3.7(满分 5 星)。46 条评论
    3.7(46)
    2.3k 名学生
    Intermediate LevelIntermediate
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    Deep Learning with PyTorch : Build an AutoEncoder
    Coursera Project Network

    新

    指导项目
    Beginner LevelBeginner
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    Practical Reinforcement Learning
    HSE University
    课程
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    评分为 4.2(满分 5 星)。414 条评论
    4.2(414)
    40k 名学生
    Advanced LevelAdvanced
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    Machine Learning for Trading
    Google Cloud
    专项课程
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    评分为 3.9(满分 5 星)。757 条评论
    3.9(757)
    28k 名学生
    Intermediate LevelIntermediate
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    Advanced Machine Learning
    HSE University
    专项课程
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    评分为 4.4(满分 5 星)。3728 条评论
    4.4(3,728)
    310k 名学生
    Advanced LevelAdvanced
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    Deep Learning and Reinforcement Learning
    IBM
    课程
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    评分为 4.8(满分 5 星)。18 条评论
    4.8(18)
    2.1k 名学生
    Intermediate LevelIntermediate
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    Self-Driving Cars
    University of Toronto
    专项课程
    Filled StarFilled StarFilled StarFilled StarHalf Filled Star
    评分为 4.7(满分 5 星)。2382 条评论
    4.7(2,382)
    110k 名学生
    Advanced LevelAdvanced
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    Tensorflow Neural Networks using Deep Q-Learning Techniques
    Coursera Project Network

    新

    指导项目
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    评分为 2.7(满分 5 星)。7 条评论
    2.7(7)
    Advanced LevelAdvanced
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    Reinforcement Learning in Finance
    New York University
    课程
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    评分为 3.5(满分 5 星)。103 条评论
    3.5(103)
    14k 名学生
    Advanced LevelAdvanced
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    Reinforcement Learning for Trading Strategies
    New York Institute of Finance
    课程
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    评分为 3.6(满分 5 星)。148 条评论
    3.6(148)
    7.6k 名学生
    Intermediate LevelIntermediate
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    Overview of Advanced Methods of Reinforcement Learning in Finance
    New York University
    课程
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    评分为 3.8(满分 5 星)。66 条评论
    3.8(66)
    7.6k 名学生
    Advanced LevelAdvanced
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    Avoid Overfitting Using Regularization in TensorFlow
    Coursera Project Network

    新

    指导项目
    Filled StarFilled StarFilled StarFilled StarFilled Star
    评分为 4.8(满分 5 星)。71 条评论
    4.8(71)
    4.2k 名学生
    Intermediate LevelIntermediate
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    Motion Planning for Self-Driving Cars
    University of Toronto
    课程
    Filled StarFilled StarFilled StarFilled StarFilled Star
    评分为 4.8(满分 5 星)。350 条评论
    4.8(350)
    24k 名学生
    Advanced LevelAdvanced
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    Prediction and Control with Function Approximation
    University of Alberta
    课程
    Filled StarFilled StarFilled StarFilled StarFilled Star
    评分为 4.8(满分 5 星)。579 条评论
    4.8(579)
    13k 名学生
    Intermediate LevelIntermediate
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    人工智慧:機器學習與理論基礎 (Artificial Intelligence - Learning & Theory)
    National Taiwan University
    课程
    Filled StarFilled StarFilled StarFilled StarHalf Filled Star
    评分为 4.7(满分 5 星)。31 条评论
    4.7(31)
    6.5k 名学生
    Intermediate LevelIntermediate
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    Using Machine Learning in Trading and Finance
    New York Institute of Finance
    课程
    Filled StarFilled StarFilled StarFilled StarStar
    评分为 3.9(满分 5 星)。241 条评论
    3.9(241)
    11k 名学生
    Intermediate LevelIntermediate

Searches related to reinforcement learning

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总之,这是我们最受欢迎的 reinforcement learning 门课程中的 10 门

  • Reinforcement Learning: University of Alberta
  • Fundamentals of Reinforcement Learning: University of Alberta
  • Deep Learning: DeepLearning.AI
  • Machine Learning and Reinforcement Learning in Finance: New York University
  • Introduction to Reinforcement Learning in Python: Coursera Project Network
  • Deep Learning with PyTorch : Build an AutoEncoder: Coursera Project Network
  • Practical Reinforcement Learning: HSE University
  • Machine Learning for Trading: Google Cloud
  • Advanced Machine Learning: HSE University
  • Deep Learning and Reinforcement Learning: IBM

您可以在 Machine Learning 中学到的技能

Python 程序设计 (33)
Tensorflow (32)
深度学习 (30)
人工神经网络 (24)
大数据 (18)
统计分类 (17)
代数 (10)
贝叶斯定理 (10)
线性代数 (10)
线性回归 (9)
Numpy (9)
推荐系统 (9)

关于 Reinforcement Learning 的常见问题

  • 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.

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