Data Science and AI

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…

FP-Growth Algorithm in Data Mining: Fast Frequent Pattern Mining Explained

What is FP-Growth (Frequent Pattern Growth)? An effective and scalable technique for mining frequent itemsets in big transactional databases is the FP-Growth (Frequent Pattern Growth) algorithm. By employing a tree-based…

Deep Belief Networks (DBN): Architecture, Training Process & Applications in Deep Learning

What is Deep Belief Networks (DBN) Algorithm? One kind of deep learning model is called a Deep Belief Network (DBN), which is composed of several layers of unsupervised learning models…

Deep Q-Networks (DQN): Reinforcement Learning with Deep Neural Networks Explained

What is Deep Q-Networks (DQN) Algorithm? The Deep Q-Network (DQN) algorithm is a reinforcement learning technique that solves issues with very large or continuous state or action spaces by combining…

Extreme Learning Machine (ELM): Fast Learning Algorithm for Single-Layer Feedforward Networks

What is Extreme Learning Machine (ELM) Algorithm? A quick and effective machine learning approach for training Single Hidden Layer Feedforward Neural Networks (SLFNs) is called Extreme Learning Machine (ELM). ELM…

K-Nearest Neighbors (KNN) Algorithm: Classification, Working & Real-World Applications

What is k-Nearest Neighbors (k-NN)? A supervised learning approach for classification and regression problems is called k-Nearest Neighbors (k-NN). Since it is instance-based and non-parametric, it does not explicitly learn…

Naive Bayes Algorithm: Working Principle, Types & Applications in Machine Learning

What is Naïve Bayes Algorithm? Naïve Bayes is a probabilistic classification algorithm that relies on Bayes’ Theorem. It assumes that all features in the dataset are independent of one another.…