What is perceptron?
The perceptron is a type of artificial neural network (ANN) that is designed to recognize patterns in data. It can be used to identify objects, classify images, and detect changes in the environment. The perceptron was invented by Frank Rosenblatt in 1957 while he was working at Cornell Aeronautical Laboratory as part of a research…
Read MoreWhat is L1 and L2 regularization in Deep Learning?
In deep learning, L1 and L2 regularization are regularization techniques used to penalize the model’s weights during the training process. This penalty discourages the model from assigning excessive importance to certain features, thereby reducing the risk of overfitting. L1 Regularization L1 regularization, also known as Lasso regularization, adds a penalty proportional to the absolute value…
Read MoreSome examples of simple gradient-based NLP models.
There are a lot of simple gradient-based NLP models that can be used to solve a variety of natural language processing tasks. Some of these include: Part-of-speech tagging: sentence parsing is a task that assigns part of speech tags to words in text and is used to analyze sentences. A task that assigns part of…
Read MoreHow is NLP revolutionizing financial services?
NLP is revolutionizing the way we interact with financial services. It’s allowing us to have a more natural conversation with our banks, and this is allowing us to do things that we couldn’t do before. How did you get into NLP? I got into NLP because I had a need, but it turns out that…
Read More5 things you must know about Computer Vision!
Computer Vision is a technology that enables machines to see, and it is one of the most important technologies in Artificial Intelligence. It is also one of the most difficult technologies to understand. for example, persons. How does machine vision work? The image data can be processed and an object in the image can be…
Read MoreWhat is upsampling and downsampling?
In a classification task, there is a high chance for the algorithm to be biased if the dataset is imbalanced. An imbalanced dataset is one in which the number of samples in one class is very higher or lesser than the number of samples in the other class. An example of an imbalanced dataset is…
Read MoreWhat is GMM and Agglomerative clustering?
A Gaussian mixture is a statistical model that assumes all the data points are generated from a linear combination of multivariate Gaussian distributions. This assumption has unknown parameters that can be estimated from the data, which we refer to as hyperparameters. Firstly, K-means employs the Gaussian distributions and centers of latent Gaussians. However, unlike K-means,…
Read MoreDifference between K-means and DBSCAN clustering?
Clustering involves grouping data points by similarity. In unsupervised machine learning, for example, data points are grouped into clusters depending on the information available in the dataset. The data items in the same clusters are similar to each other, while the items in different clusters are dissimilar. KMeans and DBScan represent 2 of the most…
Read MoreWhat is the difference between LSTM and GRU?
LSTM or Long Short Term Memory is a kind of Recurrent Neural Network that is capable of learning long-term patterns. It was developed by Schmidhuber and Hochreiter in 1997. It connects sequences of memory in a way that makes it difficult to remember each of the items for an extended period of time. A Globally…
Read MoreWhat is the difference between ANN and RNN?
ANN is a form of machine learning. It models the human brain and is a type of artificial neural network. ANNs are used to solve problems in the fields of computer vision, speech recognition, natural language processing, and other domains. .Artificial Intelligence is an umbrella term for a broad range of technologies that mimic the…
Read More