Introduction to Sparse Autoencoders Autoencoders are a type of neural network used for unsupervised learning tasks, particularly for data compression and feature extraction. They consist of an encoder and a decoder: the encoder compresses input data into a smaller representation, while the decoder attempts to reconstruct the input from this compressed representation. Autoencoders are typically
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