LSTM
Long Short-Term Memory (LSTM) is a type of recurrent neural network architecture designed to learn long-range dependencies in sequential data. LSTMs use gate mechanisms to control information flow and avoid the vanishing gradient problem.
Understanding LSTM
Long Short-Term Memory (LSTM) is a specialized recurrent neural network architecture designed to capture long-range dependencies in sequential data by using gating mechanisms that control the flow of information. Standard recurrent networks suffer from vanishing gradients that make learning long sequences difficult, but LSTM cells use input, forget, and output gates to selectively remember or discard information over many time steps. LSTMs dominated sequence modeling tasks like machine translation, speech recognition, and time series forecasting before the transformer architecture emerged. They remain relevant for applications involving streaming data and scenarios where computational resources are limited. The cell state in an LSTM acts as a conveyor belt for information, with the forget gate deciding what to discard and the input gate determining what new information to store. Bidirectional LSTMs and stacked LSTM layers further improve performance on complex sequential tasks.
Category
Deep Learning
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Activation Function
An activation function is a mathematical function applied to a neuron's output to introduce non-linearity into a neural network. Common activation functions include ReLU, sigmoid, and tanh, each with different properties for gradient flow.
Adam Optimizer
Adam (Adaptive Moment Estimation) is an optimization algorithm that combines the benefits of AdaGrad and RMSProp. It adapts learning rates for each parameter using estimates of first and second moments of gradients.
Adapter Layers
Adapter layers are small trainable modules inserted into a pre-trained model to enable parameter-efficient fine-tuning. They allow task adaptation while keeping the original model weights frozen.
Attention Mechanism
An attention mechanism allows neural networks to focus on the most relevant parts of the input when producing each element of the output. Attention is the foundational innovation behind the Transformer architecture and modern large language models.
Autoencoder
An autoencoder is a neural network trained to compress input data into a compact representation and then reconstruct it. Autoencoders are used for dimensionality reduction, denoising, and learning latent representations.
Backpropagation
Backpropagation is the algorithm used to train neural networks by computing gradients of the loss function with respect to each weight. It propagates error signals backward through the network to update weights and minimize prediction errors.
Batch Normalization
Batch normalization is a technique that normalizes layer inputs across mini-batches during training to stabilize and accelerate neural network training. It reduces internal covariate shift and allows higher learning rates.
Batch Size
Batch size is the number of training examples used in one iteration of gradient descent. Larger batches provide more stable gradient estimates but require more memory, while smaller batches add beneficial noise.