A review on the long short-term memory model. Greg Van Houdt, Carlos Mosquera, and Gonzalo Nápoles (2020) provide a comprehensive overview of the Long Short-Term Memory (LSTM) model, which has become a key component in machine learning and neurocomputing. LSTM is a type of recurrent neural network that addresses the vanishing/exploding gradient problem, enabling effective learning of long-term dependencies. The paper discusses the theoretical foundations of LSTM, its training process, and various applications in fields such as time series prediction, natural language processing, computer vision, and image/video captioning. It also covers different LSTM variants and their performance relative to the vanilla LSTM. The authors highlight the versatility of LSTM in handling temporal data and its effectiveness in tasks like speech recognition, machine translation, and fault diagnosis. The paper also includes a TensorFlow code example for predicting the next word in a short story. The review emphasizes the importance of LSTM in deep learning and its potential for future applications. The authors conclude that while the vanilla LSTM is widely used, there are still opportunities for improvement and hybrid models that combine LSTM with other architectures. The paper is a valuable resource for researchers and practitioners interested in understanding and applying LSTM in various domains.A review on the long short-term memory model. Greg Van Houdt, Carlos Mosquera, and Gonzalo Nápoles (2020) provide a comprehensive overview of the Long Short-Term Memory (LSTM) model, which has become a key component in machine learning and neurocomputing. LSTM is a type of recurrent neural network that addresses the vanishing/exploding gradient problem, enabling effective learning of long-term dependencies. The paper discusses the theoretical foundations of LSTM, its training process, and various applications in fields such as time series prediction, natural language processing, computer vision, and image/video captioning. It also covers different LSTM variants and their performance relative to the vanilla LSTM. The authors highlight the versatility of LSTM in handling temporal data and its effectiveness in tasks like speech recognition, machine translation, and fault diagnosis. The paper also includes a TensorFlow code example for predicting the next word in a short story. The review emphasizes the importance of LSTM in deep learning and its potential for future applications. The authors conclude that while the vanilla LSTM is widely used, there are still opportunities for improvement and hybrid models that combine LSTM with other architectures. The paper is a valuable resource for researchers and practitioners interested in understanding and applying LSTM in various domains.