Recurrent Neural Network
A Recurrent Neural Network (RNN) is a neural architecture designed for sequential data that maintains a hidden state across time steps. While largely superseded by Transformers, RNNs introduced the concept of memory in neural networks.
Understanding Recurrent Neural Network
A Recurrent Neural Network (RNN) is a type of neural network designed to process sequential data by maintaining a hidden state that captures information from previous time steps, allowing the model to exhibit temporal memory. Unlike feedforward networks, RNNs pass their output back as input for the next step, creating a loop that enables them to handle variable-length sequences such as text, speech, time series, and video. Standard RNNs suffer from the vanishing gradient problem, which makes it difficult to learn long-range dependencies, leading to the development of improved variants like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. RNNs were the dominant architecture for natural language processing tasks before transformers emerged, powering machine translation, speech recognition, and text generation systems. While attention-based transformers have largely superseded RNNs for most language tasks, recurrent architectures remain relevant for certain streaming and real-time applications.
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.