Recurrent Neural Networks (RNNs)
Designed for sequential data like text, audio, and time series. Explore the internal gating mechanisms of an LSTM (Long Short-Term Memory) cell that allow it to remember or forget information.
Note: This uses a character-frequency transition table (Markov chain), not an actual RNN. The LSTM cell visualization shows real gate math; this panel demonstrates the concept of sequence generation from learned transitions.
Learn Recurrent Neural Networks (RNNs) on DataCamp
Curated courses and career tracks to take your understanding from this demo to real-world mastery. All links open directly on DataCamp.
Intermediate Deep Learning with PyTorch
Build RNNs, LSTMs, and GRUs from scratch with PyTorch for sequence modeling tasks like time-series and text generation.
Deep Learning for Text with PyTorch
Use RNNs and Transformers to tackle NLP tasks: text classification, sentiment analysis, and text generation.
Introduction to Deep Learning with PyTorch
Build the foundational neural network knowledge required before working with recurrent architectures.
Machine Learning for Time Series Data in Python
Explore how to apply machine learning—including RNNs—to time series and sequential data for forecasting and anomaly detection.
Introduction to Natural Language Processing with Python
Learn to process text data with Python. Master tokenization, POS tagging, named entity recognition, and text classification.
Deep Learning in Python
Master PyTorch and TensorFlow to build sequence models, including RNNs, LSTMs, and Transformers.
