ai 项目经理 要了解_了解人工智能

ai 项目经理 要了解

了解人工智能 (Understanding Artificial Intelligence)

Artificial Intelligence includes the simulation process of human intelligence by machines and special computer systems. The examples of artificial intelligence include learning, reasoning and self-correction. Applications of AI include speech recognition, expert systems, and image recognition and machine vision.

人工智能包括通过机器和专用计算机系统进行的人类仿真过程。 人工智能的例子包括学习，推理和自我纠正。 AI的应用包括语音识别，专家系统以及图像识别和机器视觉。

Machine learning is the branch of artificial intelligence, which deals with systems and algorithms that can learn any new data and data patterns.

机器学习是人工智能的分支，它处理可以学习任何新数据和数据模式的系统和算法。

Let us focus on the Venn diagram mentioned below for understanding machine learning and deep learning concepts.

让我们专注于下面提到的维恩图，以了解机器学习和深度学习的概念。

Machine learning includes a section of machine learning and deep learning is a part of machine learning. The ability of program which follows machine learning concepts is to improve its performance of observed data. The main motive of data transformation is to improve its knowledge in order to achieve better results in the future, provide output closer to the desired output for that particular system. Machine learning includes “pattern recognition” which includes the ability to recognize the patterns in data.

机器学习包括机器学习的一部分，而深度学习是机器学习的一部分。 遵循机器学习概念的程序的能力是提高其观测数据的性能。 数据转换的主要动机是提高其知识水平，以便将来获得更好的结果，为特定系统提供更接近所需输出的输出。 机器学习包括“模式识别”，其中包括识别数据中模式的能力。

The patterns should be trained to show the output in desirable manner.

模式应经过训练以期望的方式显示输出。

Machine learning can be trained in two different ways −

机器学习可以两种不同的方式进行训练-

• Supervised training
  有监督的培训
• Unsupervised training
  无监督培训

监督学习 (Supervised Learning)

Supervised learning or supervised training includes a procedure where the training set is given as input to the system wherein, each example is labeled with a desired output value. The training in this type is performed using minimization of a particular loss function, which represents the output error with respect to the desired output system.

监督学习或监督训练包括以下过程：将训练集作为系统的输入，其中，每个示例都标有所需的输出值。 使用特定损失函数的最小化来执行这种类型的训练，该损失函数表示相对于所需输出系统的输出误差。

After completion of training, the accuracy of each model is measured with respect to disjoint examples from training set, also called the validation set.

训练完成后，针对训练集(也称为验证集)中不相交的示例，测量每个模型的准确性。

The best example to illustrate “Supervised learning” is with a bunch of photos given with information included in them. Here, the user can train a model to recognize new photos.

举例说明“监督学习”的最好例子是一堆照片，其中包含信息。 在这里，用户可以训练模型以识别新照片。

无监督学习 (Unsupervised Learning)

In unsupervised learning or unsupervised training, include training examples, which are not labeled by the system to which class they belong. The system looks for the data, which share common characteristics, and changes them based on internal knowledge features.This type of learning algorithms are basically used in clustering problems.

在无监督学习或无监督培训中，请包括培训示例，这些示例未按其所属的系统标记。 该系统寻找具有共同特征的数据，并根据内部知识特征对其进行更改。这种学习算法基本上用于聚类问题。

The best example to illustrate “Unsupervised learning” is with a bunch of photos with no information included and user trains model with classification and clustering. This type of training algorithm works with assumptions as no information is given.

最好的例子来说明“无监督学习”是一堆没有信息的照片，以及带有分类和聚类的用户训练模型。 由于没有给出任何信息，因此这种训练算法可以在假设条件下使用。

翻译自: https://www.tutorialspoint.com/tensorflow/tensorflow_understanding_artificial_intelligence.htm

ai 项目经理 要了解