Capsule Networks (CapsNets) are a relatively recent innovation in the field of deep learning, proposed to address some of the limitations of traditional Convolutional Neural Networks (CNNs) in tasks such as image recognition and computer vision. While CNNs have been the go-to architecture for image processing tasks for years, they struggle with certain challenges, particularly…
Gated Recurrent Units (GRUs) are a type of recurrent neural network (RNN) architecture that have gained significant popularity for sequential data tasks such as time-series forecasting, natural language processing (NLP), and speech recognition. GRUs were introduced as a simpler alternative to Long Short-Term Memory (LSTM) networks, offering similar capabilities in learning long-range dependencies within sequences…
Long Short-Term Memory (LSTM) networks have become a cornerstone in the world of machine learning, particularly for tasks involving sequential data. While standard LSTMs process data in one direction, from past to future, Bidirectional LSTMs (BiLSTMs) take a step further by processing data in both directions—both from the past to the future and from the…