Python Deep Learning 简明教程
Python Deep Basic Machine Learning
人工智能 (AI) 是一种让计算机模仿人类认知行为或智能的代码、算法或技术。机器学习 (ML) 是 AI 的一个子集,它使用统计方法使机器能够通过经验进行学习和改进。深度学习是机器学习的一个子集,它让多层神经网络的计算变得可行。机器学习被视为浅层学习,而深度学习被视为具有抽象功能的分层学习。
Artificial Intelligence (AI) is any code, algorithm or technique that enables a computer to mimic human cognitive behaviour or intelligence. Machine Learning (ML) is a subset of AI that uses statistical methods to enable machines to learn and improve with experience. Deep Learning is a subset of Machine Learning, which makes the computation of multi-layer neural networks feasible. Machine Learning is seen as shallow learning while Deep Learning is seen as hierarchical learning with abstraction.
机器学习涉及广泛的概念。概念如下所示 −
Machine learning deals with a wide range of concepts. The concepts are listed below −
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supervised
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unsupervised
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reinforcement learning
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linear regression
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cost functions
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overfitting
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under-fitting
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hyper-parameter, etc.
在监督学习中,我们学习根据标记数据预测值。一种能在此方面提供帮助的 ML 技术是分类,其中目标值是不连续的值;例如,猫和狗。机器学习中的另一种可能提供帮助的技术是回归。回归根据目标值工作。目标值是连续值;例如,可以使用回归来分析股市数据。
In supervised learning, we learn to predict values from labelled data. One ML technique that helps here is classification, where target values are discrete values; for example,cats and dogs. Another technique in machine learning that could come of help is regression. Regression works onthe target values. The target values are continuous values; for example, the stock market data can be analysed using Regression.
在无监督学习中,我们从未标记或未结构化的输入数据中进行推理。如果我们有数百万份医疗记录,并且我们必须理解它,找出其基本结构、异常值或检测异常情况,那么我们会使用聚类技术将数据分成广泛的群集。
In unsupervised learning, we make inferences from the input data that is not labelled or structured. If we have a million medical records and we have to make sense of it, find the underlying structure, outliers or detect anomalies, we use clustering technique to divide data into broad clusters.
数据集分为训练集、测试集、验证集等。
Data sets are divided into training sets, testing sets, validation sets and so on.
2012 年的一项突破使深度学习概念变得突出。一种算法使用 2 个 GPU 和大数据等最新技术将 100 万张图像成功分类为 1,000 个类别。
A breakthrough in 2012 brought the concept of Deep Learning into prominence. An algorithm classified 1 million images into 1000 categories successfully using 2 GPUs and latest technologies like Big Data.
Relating Deep Learning and Traditional Machine Learning
传统机器学习模型遇到的一个主要挑战是一个称为特征提取的过程。程序员需要具体说明并告诉计算机要寻找的特征。这些特征将帮助做出决策。
One of the major challenges encountered in traditional machine learning models is a process called feature extraction. The programmer needs to be specific and tell the computer the features to be looked out for. These features will help in making decisions.
将原始数据输入算法很少能够起作用,因此特征提取是传统机器学习工作流程的关键部分。
Entering raw data into the algorithm rarely works, so feature extraction is a critical part of the traditional machine learning workflow.
这给程序员带来了巨大的责任,而且算法的效率在很大程度上取决于程序员的创造力。对于对象识别或手写识别等复杂问题,这是一个巨大的问题。
This places a huge responsibility on the programmer, and the algorithm’s efficiency relies heavily on how inventive the programmer is. For complex problems such as object recognition or handwriting recognition, this is a huge issue.
深度学习具有学习多层表示的能力,是少数能够帮助我们进行自动特征提取的方法之一。可以假定较低层可以执行自动特征提取,几乎不需要程序员的指导。
Deep learning, with the ability to learn multiple layers of representation, is one of the few methods that has help us with automatic feature extraction. The lower layers can be assumed to be performing automatic feature extraction, requiring little or no guidance from the programmer.