監督學習無監督學習

To begin with, we should know that machine primarily consists of four major domain.

首先，我們應該知道機器主要由四個主要領域組成。

Supervised learning: An agent or algorithm learns from the labeled data.

有監督的學習：代理或算法從標記的數據中學習。

Unsupervised learning: An agent or algorithm learns from the unlabeled data i.e. it finds similar patterns in the dataset and groups them accordingly.

無監督學習：代理或算法從未標記的數據中學習，即，它在數據集中找到相似的模式并將其相應地分組。

Semi-supervised learning: A combination of both Supervised and Unsupervised learning.

半監督學習：監督學習和無監督學習的組合。

Reinforcement learning: An agent or algorithm learns patterns or behaviors by correcting itself over and over again until evolves into a better agent.

強化學習：代理或算法通過反復校正自身直到發展成為更好的代理來學習模式或行為。

Now let us see the methods which come under the unsupervised learning domain.

現在讓我們看看無監督學習領域下的方法。

聚類 (Clustering)

Photo by Nareeta Martin on Unsplash Nareeta Martin在Unsplash上拍攝的照片

The goal of clustering is to create groups of data points such that points in different clusters are dissimilar while points within a cluster are similar.

聚類的目的是創建數據點組，以使不同聚類中的點不相似，而聚類中的點相似。

Clustering also has its own subcategories.

群集也有其自己的子類別。

1. K-均值聚類 (1. K-means clustering)

With k-means clustering, we want to cluster our data points into k groups. A larger k creates smaller groups with more granularity, a lower k means larger groups and less granularity. It can be compared to the separate crowds of people surrounding different famous people at a party. The density of the crowd depends on the fame of that person.

通過k均值聚類，我們希望將數據點聚類為k組。 k越大，組越細，粒度越大； k越小，組越大，粒度越小。 可以將它與聚會上圍繞著不同名人的獨立人群進行比較。 人群的密度取決于那個人的名聲。

2.層次聚類 (2. Hierarchical clustering)

Hierarchical clustering is similar to regular clustering, but it focuses on building a hierarchy of clusters. This type of clustering is used in the online shopping websites, where there are broad categories for simple navigation displayed on the homepage and as you click on it, further specific categories related to that would be displayed. This explains the more distinct cluster of items.

分層群集類似于常規群集，但是它專注于構建群集的層次結構。 這種類型的群集用于在線購物網站中，在主頁上會顯示大范圍的簡單導航，并且當您單擊它時，將顯示與之相關的其他特定類別。 這解釋了更獨特的項目集群。

降維 (Dimensionality-reduction)

1.主成分分析： (1. Principal Component Analysis:)

PCA is a dimensionality-reduction method in unsupervised learning which is used to reduce the dimensionality of large data sets into smaller ones by choosing the basis vectors on our own which are known as principal components. PCA remaps the space in which our data exists to make it more compressible. The transformed dimension is smaller than the original dimension.

PCA是一種無監督學習中的降維方法，用于通過自行選擇被稱為主要成分的基礎向量，將大數據集的維數減少為較小的數據集。 PCA重新映射了我們數據存在的空間，以使其更具可壓縮性。 變換后的尺寸小于原始尺寸。

2. K近鄰 (2. K-nearest neighbor)

How do you determine the housing price of a house in a particular locality? We would take the average of the price of the houses in the nearby locality and determine the approximate price of the house we are about to buy. We label the test data point based on the average of the sample data in its neighborhood. We take the mean of the values if the variables are continuous and mode if they are categorical.

您如何確定特定地區房屋的房價？ 我們將取附近地區房屋平ASP格，并確定我們將要購買的房屋的近似價格。 我們根據附近的樣本數據的平均值來標記測試數據點。 如果變量是連續的，則取值的平均值；如果變量是分類的，則取值的平均值。

Applications of k-NN:

k-NN的應用：

• Helps in the update of new methods of fraud detection.
  幫助更新欺詐檢測的新方法。
• Determining the housing price and detection of the temperature in the locality.
  確定房屋價格并檢測當地溫度。
• Imputing missing training data.
  估算缺少的訓練數據。

3. T分布隨機鄰居嵌入 (3. T-distributed Stochastic Neighbor Embedding)

t-SNE Embedding is an algorithm used to reduce a high dimensional dataset into a low dimensional graph that retains most of the original information. It is based on the principle of determining the similarity of all points in the scatter plot.

t-SNE嵌入是一種用于將高維數據集還原為保留大部分原始信息的低維圖形的算法。 它基于確定散點圖中所有點的相似性的原理。

The process done here is measuring the distance from the point we are interested in all the other points and plotting that distance on a normal distribution curve, which is centered on the point that we are interested in.

此處完成的過程是測量到我們在所有其他點上都感興趣的點的距離，并將該距離繪制在正態分布曲線上，該分布曲線以我們感興趣的點為中心。

Note: We use a normal distribution curve because distant points have low similarity values and close points have high similarity values.

注意：我們使用正態分布曲線，因為遠點的相似度值低而閉合點的相似度值高。

Now it puts the data points on a number line in a random order, and t-SNE moves these points little by little based on their similarity values, until it has clustered them properly on a lower dimension.

現在，它將數據點以隨機順序放置在數字線上，然后t-SNE根據它們的相似性值一點一點地移動這些點，直到將它們正確地聚集在較低維度上為止。

生成建模 (Generative modeling)

1.生成對抗網絡 (1. Generative adversarial network)

A generative adversarial network is deep learning-based generative model. Generative models are models that use unsupervised learning. GAN is a system where two neural networks compete to create or generate variations within a dataset.

生成對抗網絡是基于深度學習的生成模型。 生成模型是使用無監督學習的模型。 GAN是一個系統，其中兩個神經網絡競爭在數據集中創建或生成變體。

It has a generator model and a discriminator model. The generator network takes a sample and generates a sample of data by learning the distribution of classes. The discriminator network learns the boundaries between those classes by estimating the probability of whether the sample is taken from the real sample.

它具有生成器模型和鑒別器模型。 生成器網絡通過學習類的分布來獲取樣本并生成數據樣本。 鑒別器網絡通過估計是否從真實樣本中提取樣本的概率來學習這些類別之間的界限。

GAN的應用： (Applications of GAN :)

• They are used for image manipulation and generation.
  它們用于圖像處理和生成。
• They can be deployed for tasks in understanding risk and recovery in healthcare.
  可以將它們部署用于了解醫療保健的風險和恢復的任務。
• Used in drug research to produce new chemical structures from the existing ones.
  用于藥物研究以從現有化學結構產生新的化學結構。
• Google brain project is an interesting application of GAN.
  Google的大腦項目是GAN的有趣應用。

The main advantage of GAN is to generate data when there is not much data available, without any human supervision.

GAN的主要優點是在沒有可用數據的情況下在沒有任何人工監督的情況下生成數據。

2.深度卷積生成對抗網絡 (2. Deep Convolutional Generative adversarial Network)

DCGAN has convolutional layers between the input and the output image in the generator. And in the discriminator, it uses regular convolutional networks to classify the generated and the real images. The architecture of the DCGAN is:

DCGAN在生成器的輸入和輸出圖像之間具有卷積層。 在鑒別器中，它使用常規的卷積網絡對生成的圖像和真實圖像進行分類。 DCGAN的體系結構為：

• The pooling layers are replaced with generators and discriminators.
  合并層被生成器和鑒別器代替。
• Batch normalization is used in both generators and discriminators.
  批處理規范化在生成器和鑒別器中都使用。
• The fully connected layers are removed.
  完全連接的層將被刪除。
• ReLU is used as the activation function in the generator for all layers except the output layer.
  ReLU用作生成器中除輸出層以外的所有層的激活函數。
• Leaky ReLU activation function is used in the discriminator for all layers.
  鑒別器對所有層使用泄漏的ReLU激活功能。

3.樣式轉移 (3. Style Transfer)

Style transfer is the method used to generate a new image by combining the content image with a style image. By using this we can make the environment image that we have looked a lot greater because it is being combined with the style of iconic paintings.

樣式轉移是用于通過將內容圖像與樣式圖像組合來生成新圖像的方法。 通過使用它，我們可以使我們看起來更大的環境圖像，因為它已與標志性繪畫的風格相結合。

The activations in the neural network of the content and the style image should match the activations in the generated image. So style transfer can make any image that you took on your trek look modified like the famous Hokusai Japanese painting.

內容和樣式圖像在神經網絡中的激活應與生成的圖像中的激活匹配。 因此，樣式轉移可以使您在跋涉中拍攝的任何圖像看起來都像著名的北齋日本畫一樣被修改。

翻譯自: https://medium.com/perceptronai/a-brief-introduction-to-unsupervised-learning-a18c6f1e32b0

監督學習無監督學習

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