LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) architecture that is particularly well-suited for sequence prediction tasks. Unlike traditional RNNs, LSTMs are designed to capture long-range dependencies in sequential data. They use memory cells to store information over time, allowing them to learn from both short-term and long-term patterns in data. LSTMs are widely used in applications such as language modeling, time series forecasting, and speech recognition, where understanding the context of a sequence is crucial. In this video, we’ll explore how LSTM works, its structure, and why it's so powerful in handling sequence data. We’ll also look at practical examples of how LSTM models can be used for tasks like text generation and sentiment analysis. #LSTM #MachineLearning #DeepLearning #NeuralNetworks #RNN #AI #DataScience #TimeSeries #NaturalLanguageProcessing #TextGeneration #SequencePrediction #ArtificialIntelligence #MachineLearningAlgorithms #TechExplained #AIforBeginners #DeepLearningModels