术语 (Terminologies)

介绍 (Introduction)

I’ve recently gone through a set of machine learning-based projects presented in Juptyter notebook and have noticed that there are a set of recurring terms and terminologies in all notebooks and machine learning-based projects I’ve worked on or reviewed.

我最近浏览了Juptyter笔记本中介绍的一组基于机器学习的项目，并注意到在我从事或研究的所有笔记本和基于机器学习的项目中都有一组重复的术语和术语。

You can see this article as a way of cutting through some noise within machine learning and deep learning. Expect to find descriptions and explanations of terms and terminologies that you are bound to come across in the majority of deep learning-based projects.

您可以将本文视为消除机器学习和深度学习中的噪音的一种方式。 期望找到对大多数基于深度学习的项目必定会遇到的术语和术语的描述和解释。

I cover the definition of terms and terminologies associated with the following subject areas in a machine learning project:

我将介绍与机器学习项目中以下主题领域相关的术语和术语的定义：

1. Datasets

   数据集

2. Convolutional Neural Network Architecture

   卷积神经网络架构

3. Techniques

   技巧

4. Hyperparameters

   超参数

1.数据集 (1. Datasets)

Training Dataset: This is the group of our dataset used to train the neural network directly. Training data refers to the dataset partition exposed to the neural network during training.

训练数据集 ：这是我们的数据集，用于直接训练神经网络。 训练数据是指训练期间暴露于神经网络的数据集分区。

Validation Dataset: This group of the dataset is utilized during training to assess the performance of the network at various iterations.

验证数据集 ：在训练期间利用该组数据集来评估网络在各种迭代中的性能。

Test Dataset: This partition of the dataset evaluates the performance of our network after the completion of the training phase.

测试数据集 ：在训练阶段完成后， 数据集的此分区评估了我们网络的性能。

2.卷积神经网络 (2. Convolutional Neural Networks)

Convolutional layer: A convolution is a mathematical term that describes a dot product multiplication between two sets of elements. Within deep learning, the convolution operation acts on the filters/kernels and image data array within the convolutional layer. Therefore a convolutional layer simply houses the convolution operation that occurs between the filters and the images passed through a convolutional neural network.

卷积层 ：卷积是一个数学术语，用于描述两组元素之间的点积乘法。 在深度学习中，卷积操作作用于卷积层内的滤镜/内核和图像数据阵列。 因此，卷积层仅容纳发生在滤波器和通过卷积神经网络的图像之间发生的卷积运算。

Batch Normalization layer: Batch Normalization is a technique that mitigates the effect of unstable gradients within a neural network through the introduction of an additional layer that performs operations on the inputs from the previous layer. The operations standardize and normalize the input values, after that the input values are transformed through scaling and shifting operations.

批处理归一化层 ：批处理归一化是一种技术，它通过引入一个附加层来减轻神经网络中不稳定梯度的影响，该附加层对来自上一层的输入执行操作。 在通过缩放和移位操作对输入值进行转换之后，这些操作将对输入值进行标准化和标准化。

MaxPooling layer: Max pooling is a variant of sub-sampling where the maximum pixel value of pixels that fall within the receptive field of a unit within a sub-sampling layer is taken as the output. The max-pooling operation below has a window of 2x2 and slides across the input data, outputting an average of the pixels within the receptive field of the kernel.

MaxPooling层 ：Max pooling是子采样的一种变体，其中将属于子采样层内某个单元的接收场内的像素的最大像素值作为输出。 下面的最大池化操作具有2x2的窗口，并在输入数据上滑动，输出内核接受域内像素的平均值。

Flatten layer: Takes an input shape and flattens the input image data into a one-dimensional array.

拼合层 ：采用输入形状并将输入图像数据拼合为一维数组。

Dense Layer: A dense layer has an embedded number of arbitrary units/neurons within. Each neuron is a perceptron.

致密层 ：致密层中嵌入了任意数量的单元/神经元。 每个神经元都是一个感知器。

3.技术 (3. Techniques)

Activation Function: A mathematical operation that transforms the result or signals of neurons into a normalized output. The purpose of an activation function as a component of a neural network is to introduce non-linearity within the network. The inclusion of an activation function enables the neural network to have greater representational power and solve complex functions.

激活函数 ：将神经元的结果或信号转换为标准化输出的数学运算。 激活函数作为神经网络的组成部分的目的是在网络内引入非线性。 包含激活函数使神经网络具有更大的表示能力并能够解决复杂的函数。

Rectified Linear Unit Activation Function(ReLU): A type of activation function that transforms the value results of a neuron. The transformation imposed by ReLU on values from a neuron is represented by the formula y=max(0,x). The ReLU activation function clamps down any negative values from the neuron to 0, and positive values remain unchanged. The result of this mathematical transformation is utilized as the output of the current layer and used as input to a consecutive layer within a neural network.

整流线性单位激活函数(ReLU) ：一种激活函数，可转换神经元的值结果。 ReLU对来自神经元的值施加的变换由公式y = max(0，x)表示 。 ReLU激活功能将神经元的任何负值钳制为0，而正值保持不变。 该数学变换的结果被用作当前层的输出，并被用作神经网络内连续层的输入。

Softmax Activation Function: A type of activation function that is utilized to derive the probability distribution of a set of numbers within an input vector. The output of a softmax activation function is a vector in which its set of values represents the probability of an occurrence of a class or event. The values within the vector all add up to 1.

Softmax激活函数 ：一种激活函数，用于导出输入向量内一组数字的概率分布。 softmax激活函数的输出是一个向量，其中的一组值表示发生类或事件的概率。 向量中的值总计为1。

Dropout: Dropout technique works by randomly reducing the number of interconnecting neurons within a neural network. At every training step, each neuron has a chance of being left out, or rather, dropped out of the collated contributions from connected neurons.

辍学 ：辍学技术通过随机减少神经网络中互连神经元的数量来工作。 在每个训练步骤中，每个神经元都有机会被遗漏，或更确切地说，会从连接的神经元的整理贡献中消失。

4.超参数 (4. Hyperparameters)

Loss function: A method that quantifies ‘how well’ a machine learning model performs. The quantification is an output(cost) based on a set of inputs, which are referred to as parameter values. The parameter values are used to estimate a prediction, and the ‘loss’ is the difference between the predictions and the actual values.

损失函数 ：一种方法，量化机器“ 如何 ”学习模型执行。 量化是基于一组输入的输出(成本)，称为参数值。 参数值用于估计预测，而“损失”是预测与实际值之间的差。

Optimization Algorithm: An optimizer within a neural network is an algorithmic implementation that facilitates the process of gradient descent within a neural network by minimizing the loss values provided via the loss function. To reduce the loss, it is paramount the values of the weights within the network are selected appropriately.

优化算法 ：神经网络内的优化器是一种算法实现，可通过最小化通过损失函数提供的损失值来促进神经网络内的梯度下降过程。 为了减少损失，最重要的是适当选择网络内的权重值。

Learning Rate: An integral component of a neural network implementation detail as it’s a factor value that determines the level of updates that are made to the values of the weights of the network. Learning rate is a type of hyperparameter.

学习率 ：神经网络实现细节的组成部分，因为它是决定对网络权重值进行更新的级别的因子值。 学习率是一种超参数。

Epoch: This is a numeric value that indicates the number of time a network has been exposed to all the data points within a training dataset.

时代：这是一个数字值，表示网络暴露于训练数据集中所有数据点的时间。

结论 (Conclusion)

There are obviously tons more terms and terminologies that you are bound to come across as you undertake and complete machine learning projects.

在您完成并完成机器学习项目时，显然会有更多的术语和术语。

In future articles, I’ll probably expand on more complex concepts within machine learning that appear frequently.

在以后的文章中，我可能会扩展机器学习中经常出现的更复杂的概念。

Feel free to save the article or share it with machine learning practitioners who are at the start of their learning journey or career.

随意保存文章或与处于学习旅程或职业开始的机器学习从业者分享。

我希望您觉得这篇文章有用。 (I hope you found the article useful.)

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   订阅我的电子邮件列表 每周通讯

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