Anomaly detection or outlier detection is identifying data points, events, or observations that deviate significantly from the majority of the data and do not follow a pre-defined notion of normal behavior. It is carried out to prevent fraud and to create a secure system or model. But before we talk about anomaly detection, let us…
Autoencoders are a type of neural network used in unsupervised learning. They encode the input data to a lower-dimensional vector and attempt to reconstruct the input from the vector. So, our output is the same as the input. They are feature selective, which ensures that they can prioritize and learn the important features in the…
A radial basis function(RBF) is a real-valued function whose value depends only on the input and its distance from some fixed point (c) or the origin. The function satisfies the criteria below: The distance is usually the Euclidean distance between two points. The Euclidean distance is calculated as follows: The sum of radial basis functions is…
ResNet is a type of artificial neural network that is typically used in the field of image recognition. It has been presented as an alternative to deeper neural networks, which are quite difficult to train. The first problem with deeper neural networks was the vanishing/exploding gradients problem. When this was resolved with optimizers, a degradation(of…
Collaborative filtering is a system that predicts user behavior based on historical user data. From this, we can understand that this is used as a recommendation system. For example, Amazon recommends products or gives discounts based on historical user data or YouTube recommends videos based on your history. Why do we need this though? We…
Autoencoders are a type of neural network used in unsupervised learning. An autoencoder consists of 2 main components: encoder and decoder. The encoder encodes the input data to a lower-dimensional vector and the decoder then reconstructs the input from the vector. So, our output is the same as the input. They are feature selective, which…
When you think about cost, what comes to your mind? Cost is the estimated price that we have to pay for a service. Likewise, a cost function measures the estimated tradeoff of the accuracy of a “cut” that’s taken by the model for predicting our desired values. A cost or a loss function is a…
Maximum Likelihood Estimation (MLE) is a technique used to estimate the parameters of a statistical model. But what are parameters? A parameter is a variable whose value can be estimated from historical data. For example, in the case of Linear regression (see our article on linear regression), the distribution is Y=mx+b, the parameters are m…
A hidden Markov model (HMM) is one in which you observe a sequence of emissions, but do not know the sequence of states the model went through to generate the emissions. We analyze the hidden Markov models to recover the sequence of states from the observed data. You can read more about it from our earlier…
Let’s say, you want to create a model that can differentiate if the animal in an image is a cat or a dog. We immediately think of a convolutional neural network, but why can’t we use a neural network? They both are pretty similar… They both make use of neurons as their basic functional unit…
