Learn to build and train Recurrent Neural Networks (RNNs), including LSTMs and GRUs, for effective sequence modeling tasks like time series analysis and natural language processing. This course covers the fundamental architectures, training techniques, and practical implementation using popular deep learning frameworks.
Prerequisites Python & ML concepts
Level:
RNN Fundamentals
Understand the architecture and operation of simple Recurrent Neural Networks.
Training RNNs
Grasp the concept of Backpropagation Through Time (BPTT) and challenges like vanishing/exploding gradients.
Advanced Architectures
Learn the structure and function of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks.
Implementation
Implement RNNs, LSTMs, and GRUs using standard deep learning libraries.
Sequence Data Handling
Prepare and preprocess sequence data (text, time series) for input into recurrent models.
Sequence Modeling Tasks
Apply RNNs to common sequence modeling problems like classification, prediction, and generation.