RNN
An RNN (Recurrent Neural Network) is a class of neural networks where connections between nodes form cycles, allowing the network to maintain temporal state. While effective for sequences, RNNs struggle with long-range dependencies compared to Transformers.
Understanding RNN
RNN, or Recurrent Neural Network, is a class of neural networks specifically architected to process sequential and time-series data by maintaining internal memory through recurrent connections. At each time step, an RNN takes the current input along with its previous hidden state to produce an output and an updated hidden state, creating a chain of computations that can theoretically capture dependencies across entire sequences. Early RNN variants struggled with the vanishing gradient problem, which led to the development of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells that use gating mechanisms to control information flow. RNNs powered the first wave of neural machine translation, speech recognition, and text generation systems before transformer-based architectures demonstrated superior performance through parallelized attention mechanisms. Despite being largely superseded by transformers for natural language processing tasks, RNNs and their variants remain actively used in time-series forecasting, audio processing, and scenarios requiring low-latency sequential inference on resource-constrained devices.
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.