ARTIFICIAL INTELLIGENCE

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📝 1.Artifical Intelligence

1.1 人工智能定义？

What’s the relationship with of AI(ArtificialIntellegence),RI(ReinforcementLearning),ML(MachineLearning),DL(DeepLearning)?

Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason. Although there are as yet no AIs that match full human flexibility over wider domains or in tasks requiring much everyday knowledge, some AIs perform specific tasks as well as humans.

Pic coming from scholarly articles： 1810.06339.pdf (arxiv.org)

1.2 Machine Learning

深度学习通常分类为监督、非监督、强化学习。监督学习需要已知的标记数据来训练模型，例如分类、回归等任务。无监督学习则没有标记数据，需要从数据中自动发现模式和规律，例如聚类、降维等任务。半监督学习则结合了监督学习和无监督学习，利用少量的标记数据和大量的未标记数据来训练模型领域：推荐系统、广告投放、自然语言处理和图像识别等领域。

Usually we categorize machine learning as supervised, unsupervised, and reinforcement learning.In supervised learning, there are labeled data; in unsupervised learning, there are no labeled data.Classification and regression are two types of supervised learning problems, with categorical and numerical outputs respectively

1.3 Deep Learning

深度学习是机器学习的一种特殊形式，通过多层神经网络来学习数据表示和特征提取。深度学习通常需要更多的计算资源和数据来训练，但可以产生更好的结果。可以应用于各种领域，例如计算机视觉、自然语言处理和语音识别等。领域：计算机视觉、自然语言处理和语音识别等。它已经在图像识别、自然语言处理和机器翻译等任务中取得了很好的效果

Deep learning, or deep neural networks, is a particular machine learning scheme, usually for supervised or unsupervised learning, and can be integrated with reinforcement learning, for state representation and/or function approximator. Supervised and unsupervised learning are usually one-shot,myopic, considering instant rewards; while reinforcement learning is sequential, far-sighted, considering long-term accumulative rewards.

1.4 Reinforcement Learning

深度学习是机器学习的一种特殊形式，通过多层神经网络来学习数据表示和特征提取。深度学习通常需要更多的计算资源和数据来训练，但可以产生更好的结果。核心是深度神经网络，它可以处理高维数据，例如图像、声音等。深度神经网络通常由多个层次组成，每一层都负责对输入数据进行不同的变换和抽象，从而逐步学习数据表示和特征提取。领域：强化学习应用广泛，例如机器人控制、游戏玩家和自适应控制等领域

Reinforcement learning is usually about sequential decision making. In reinforcement learning, incontrast to supervised learning and unsupervised learning, there are evaluative feedbacks, but no supervised labels. Comparing with supervised learning, reinforcement learning has additional challenges like credit assignment, stability, and, exploration. Reinforcement learning is kin to optimal control (Bertsekas, 2012; Sutton et al., 1992), and operations research and management (Powell,2011), and is also related to psychology and neuroscience (Sutton and Barto, 2018).

1.5 Neural Network

神经网络是深度学习的基本组成部分，它是由多个神经元组成的网络。神经网络可以用于监督学习和无监督学习等任务。

Deep learning, or deep neural networks, is a particular machine learning scheme, usually for supervised or unsupervised learning, and can be integrated with reinforcement learning, for state representation and/or function approximator. Supervised and unsupervised learning are usually one-shot, myopic, considering instant rewards; while reinforcement learning is sequential, far-sighted, considering long-term accumulative rewards.

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