Data Science and Machine Learning (Theory and Projects) A to Z - Introduction - Deep learning: Recurrent Neural Networks

This course on recurrent neural networks (RNNs) covers fundamental concepts, architectures, and applications. It distinguishes itself by focusing on identifying problems best suited for RNNs, providing live coding sessions, and using real datasets. The course also addresses advanced topics like the vanishing gradient problem and includes recent models and datasets. It offers a comprehensive overview of deep learning and RNNs, with optional sections for technical details. The course aims to equip learners with the knowledge to apply RNNs effectively in various applications.

What is the significance of live coding in this course?

How does the course prepare students for diving into the field of recurrent neural networks?

What are some of the recent models and datasets used in the course projects?