Data Science and AI

Gradient Boosting Algorithm

Gradient Boosting is one of those rare ideas in machine learning that’s both simple at its core and remarkably powerful in practice. From credit scoring and click-through prediction to anomaly…

What is the Girvan–Newman Algorithm? The Girvan–Newman (GN) algorithm is a classic community detection method for networks (graphs). It identifies clusters (communities) by iteratively removing edges that act as “bridges”…

Linear Regression

What is the Linear Regression Algorithm? Linear regression is a supervised learning algorithm that models the relationship between one or more input variables (features) and a continuous output variable (target)…

Top 10 Research Areas in Natural Language Processing (NLP)

Natural Language Processing (NLP) is one of the most dynamic fields in artificial intelligence, driving breakthroughs in communication between humans and machines. This blog explores the Top 10 Research Areas…

Apriori Algorithm in Data Mining: Working, Applications & Association Rule Learning

What is Apriori Algorithm? Large transactional databases can be mined for frequent itemsets and association rules using the traditional Apriori approach. Finding patterns of items that commonly co-occur in transactions…

Gated Recurrent Unit (GRU): Architecture, Working & Applications in Deep Learning

What is Gated Recurrent Unit (GRU) Algorithm? A particular kind of Recurrent Neural Network (RNN) architecture called the Gated Recurrent Unit (GRU) is used to simulate sequential data, including speech,…

Generative Adversarial Networks (GANs): Architecture, Working & Applications in AI

What is Generative Adversarial Networks (GANs)? Ian Goodfellow invented Generative Adversarial Networks (GANs) in 2014 as a kind of machine learning technique for generative modeling. Their main purpose is to…

MobileNet Architecture: Lightweight Deep Learning Model for Mobile and Edge Devices

What is MobileNet (MobileNetV1, V2, V3) Google created the MobileNet family of lightweight, effective convolutional neural network (CNN) architectures for embedded and mobile vision applications. MobileNet models’ primary objective is…

Autoencoders (AE) in Deep Learning: Architecture, Types & Real-World Applications

What is Autoencoders (AE) Algorithm? An Artificial Neural Network type called an Autoencoder (AE) is used for unsupervised learning. Its objective is to learn effective data representations, usually for feature…

Future Research Directions in Big Data Analytics

Big data’s introduction has completely changed how information is processed and decisions are made in a variety of fields, including the social sciences, astrophysics, healthcare, and finance. The difficulties in…