Deep Learning

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…

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…

Multilayer Perceptron (MLP): Architecture, Working & Applications in Neural Networks

What is Multilayer Perceptron (MLP) ? An input layer, one or more hidden layers, and an output layer are among the several layers of neurons that make up a multilayer…

Artificial Neural Networks (ANN): Architecture, Learning Process & Applications in AI

ANN Algorithm A computational model called an artificial neural network (ANN) is modeled after the composition and operations of the human brain. It is a crucial part of deep learning…

The Foundations of Deep Learning

Because deep learning can learn from and make predictions based on large volumes of data, it has become a game-changing technology that has allowed for substantial breakthroughs in a variety…