Deep Learning - Recurrent Neural Networks with TensorFlow - Sequence Data

The video tutorial introduces sequence data and its significance in deep learning, highlighting examples like time series, weather, and audio. It discusses the challenges of weather forecasting due to chaos theory and explores sequential data in audio and text. The tutorial explains data representation in RNNs using N, T, and D dimensions and addresses handling variable length sequences in TensorFlow, emphasizing the use of padding for uniformity.

Explain the concept of time series data with an example.

Discuss the role of deep learning in handling sequential data.

What challenges arise when predicting stock prices using sequence data?

What are the implications of using constant length sequences in Tensorflow?