LSTM is a type of recurrent neural network (RNN) designed to handle sequential data and capture long-term dependencies. It's especially useful for time series forecasting, natural language processing, and stock prediction. PyTorch makes it easy to implement LSTMs with its built-in module.
Retrieval Augmented Generation refers to a system design pattern that combines external knowledge retrieval with a language model to improve the accuracy, relevance, and reliability of AI responses.
LSTM models outperform traditional RNNs in many sequence learning tasks by effectively capturing both short-term patterns and long-term dependencies. They are widely used in modern AI systems, often as a backbone for tasks requiring contextual understanding over time.
Variance measures how much a model's predictions change with different training data. High variance means the model overfits and captures noise. It's key in the bias-variance tradeoff to balance model accuracy and generalization.
Cosine similarity measures the similarity between two vectors by calculating the cosine of the angle between them. It is widely used in text matching, search engines, recommendation systems, and AI models to compare documents, queries, and user preferences.
The vanishing gradient problem happens when training deep networks or RNNs, where the gradients (used to update weights) become very small as they move backward through the network. This makes it hard for earlier layers or time steps to learn anything, causing the model to forget long-term patterns.
Recurrent Neural Network is a type of neural network that is designed to work with data that comes in sequences, like a list of words, time-series data, or steps in a process.
Backpropagation Through Time (BPTT) is an extension of the standard backpropagation algorithm used to train recurrent neural networks (RNNs). It works by unrolling the RNN over a sequence of time steps and calculating gradients for each time step.
Backpropagation is a fundamental algorithm used to train artificial neural networks. It is used to minimize the error between the predicted output and the actual output by adjusting the network's weights.
