RNN Presentation

Currently RNN is uniquely suited to model memory units. This can be used in time series forecasting, text recommenders (autocorrect), and speech recognition to identify data correlations or patterns.

Within a 4- to 6-minute presentation, make sure to include the following:

1. Explain the basic concepts of RNNs (i.e., types of datasets, widely used libraries, convolutional layers, and pooling).
2. Explain the architecture of an RNN sequence (biological behavior). Include at least two real-world examples of implementation to support your explanation.
3. Describe two challenges when training with RNN models. Provide examples to support your rationale.

Provide supporting evidence from at least two resources other than the textbook.

Keep text on slides to a minimum, utilizing the Notes section at the bottom of the slides to explain what is being presented on each slide.

In addition, make sure to integrate a variety of visuals and evidence to support the presentation.

Provide:

1. Detailed and cited content (to include a brief introduction and a reference section containing a minimum of three academic resources)
2. A comprehensive and consistent focus throughout the presentation

Effective communication in awareness of the audience