Data Science and Machine Learning (Theory and Projects) A to Z - RNN Architecture: Introduction to Module

This module introduces recurrent neural networks (RNNs), focusing on their ability to model sequences with infinite memory. It explains the concept of weight sharing and unrolling in RNNs, simplifying their complexity while addressing challenges like overfitting and the vanishing gradient problem. The module covers different RNN architectures, including one-to-many, many-to-one, and many-to-many, and discusses deep RNNs, highlighting their depth across time and layer stacking. By mastering these concepts, viewers will be able to model any sequence modeling problem using appropriate RNN architectures.

Discuss the challenges associated with recurrent neural networks, such as overfitting and the vanishing gradient problem.

What does it mean to unroll a recurrent neural network in time?

In what ways can recurrent neural networks be applied to sequence modeling problems?