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- Here we have two clusters, a dense set of points, and one cluster, a loose set of points. - In this case, K-Means Clustering cannot cope with different densities such as the figure below. Density-based spatial clustering of applications with noise (DBSCAN) - The algorithm is based on the Density-based clustering.- Can find an arbitrary shape of cluster.- It can cope with different densities ..

K-Means Clustering(KMC) - Partitional clustering approach- It means finding a group (cluster) of specific similar objects by dividing the entire space by part.- Each cluster is associated with a centroid(center of the cluster)- Each point is assigned to the cluster with the closest centroid- Number of clusters(hyperparameters), K, must be specified\begin{align*} \mathbf{X} &= C_1 \cup C_2 \cup \..

Transfer learning A technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. In other words, the re-use of a pre-trained model on a new problem. For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks. The general idea is to use the knowle..

Backpropagation Whereas Logistic Regression is made of a single layer of weights, Neural Networks are made of many layers with non-linear activation. We need to have a mechanism to update the weights of the neurons using the gradient of the loss. Such a mechanism is called Backpropagation. It propagates the gradient from the last layer to the first one. chain rule It allows you to decompose the ..

Sigmoid With only two classes, performs exactly the same operation as Softmax. Logits It refers to the stage before passing the final Sigmoid or Softmax, that is, the Raw Output of the Model. Sigmoid and Softmax receive the Logits to safely perform the operation. Softmax For multi-label available only, where one object can have multiple classes at the same time. 1. Softmax case1. 1d array. step1..