{"department":"Data Science and Analytics","description":"

Recurrent Neural Networks (RNNs) are a type of artificial neural network specialized in handling sequential data by maintaining internal state across time steps. Unlike standard feedforward networks, RNNs use loops to retain information from prior inputs, making them effective for tasks where context and order matter.

They are widely used in natural language processing (NLP), speech recognition, time series forecasting, and machine translation. Common applications include language modeling, sentiment analysis, handwriting recognition, and sequence generation. Variants such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) address limitations in standard RNNs, particularly the vanishing gradient problem, enabling better learning over long sequences.

Processes input sequences with temporal dependencies
Commonly applied in NLP and time series analysis
Utilizes backpropagation through time for training
Often implemented using frameworks like TensorFlow and PyTorch
Requires sequence padding and tokenization for text data

Professionals skilled in RNNs are expected to understand sequence modeling, data preprocessing for variable-length inputs, and techniques for managing long-term dependencies. They should be proficient in Python, deep learning libraries, and model evaluation metrics such as perplexity or mean squared error. This skill is frequently sought in data science, machine learning engineering, and research roles within tech, finance, healthcare, and academic sectors.

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