Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) designed to model sequential data. They are particularly effective at learning long-term dependencies in data sequences, which is a common challenge in traditional RNNs. LSTMs achieve this by incorporating memory cells and gates that regulate the flow of information, allowing them to retain information over extended periods. Common use cases for LSTMs include natural language processing tasks such as language translation, speech recognition, and time series forecasting, where understanding context and sequence is crucial.
Learn about L1 Regularization, a technique to prevent overfitting in machine learning by encouraging...
AI FundamentalsL2 Regularization is a technique used to prevent overfitting in machine learning by adding a penalty...
AI FundamentalsLabel smoothing is a technique used in deep learning to improve model generalization by softening ta...
AI FundamentalsDiscover the concept of language modeling in NLP, its characteristics, and common use cases.
AI Fundamentals