Clustering is one of the most common exploratory data analysis techniques used to get an intuition about the structure of the data. K-means clustering is one of the simplest and most popular unsupervised machine learning algorithms. k-Means Clustering is an algorithm that, given a dataset, will identify which data points belong to…
K-NN stands for K-Nearest Neighbors, and it’s a type of learning method where the algorithm doesn’t really “train” the data in the usual way. Instead, it memorizes instances from the training dataset and uses these instances directly to make predictions. It is known as “lazy learning”. It is a type…
In machine learning, handling missing values is a common challenge. Not all algorithms can handle missing values naturally, but some have been designed or adapted to do so. Here’s an explanation of a few such algorithms: Decision Trees and Random Forests: Decision trees inherently handle missing values. During training, if…
A Convolutional Neural Network (ConvNet/CNN) is a specialized type of neural network model designed for working with two-dimensional image data. They can be used with one-dimensional and three-dimensional data also. Some of the areas where CNNs have widely used are image recognition, image classifications. Objects detections recognize faces, etc. CNN image classifications…
Recurrent Neural Network(RNN) s are specially designed to work with sequential data, i.e. data where there is a natural notion of a ‘sequence’ such as text, videos (sequences of images), speech, etc. RNN is a type of Neural Network where the output from the previous step is fed as input to the current step. In…
LSTM networks are an extension of recurrent neural networks (RNNs). They can remember the information for a long period of time, unlike RNNs. In RNNs, there is no finer control over which part of the context needs to be carried forward or ‘forgotten’. The other issues with RNNs occur during…
The training samples are passed through the network and the output obtained from the network is compared with the actual output. This error is used to change the weights of the neurons such that the error decreases gradually. This is done using the Backpropagation algorithm, also called backprop. Iteratively passing batches of…
In a feedforward neural network, the output from one layer is used as input to the next layer. Forward propagation means movement happens only from input to the output. This means there are no loops in the network and information is always fed forward. Input data passes into a layer where…
An activation function helps a neural network to learn complex relationships and patterns in data. It takes in the output signal from the previous cell and converts it into some form that can be taken as input to the next cell. The activation function introduces non-linearity into the output of a neuron….
Artificial Neural networks(ANN’s) are the base or functional unit of deep learning. A neural network emerged from a very popular machine learning algorithm named perceptron. A Neuron is the basic unit of computation in a neural network. It is also called as a node or unit. The leftmost layer in this network is…