With the exponential growth of user-generated data, mastering RNNs is essential for deep learning engineers to perform tasks like classification and prediction. Architectures such as RNNs, GRUs, and LSTMs are top choices, making mastering RNNs a priority. This course starts with the basics and gradually builds your theoretical and practical skills to build, train, and implement RNNs. You will engage in several exercises on topics like gradient descents in RNNs, GRUs, and LSTMs, and learn to implement RNNs using TensorFlow.

The course concludes with two exciting and realistic projects: creating an automatic book writer and a stock price prediction application. By the end, you will be equipped to confidently use and implement RNNs in your projects. No prior RNN knowledge is required; Python experience is helpful.

This course is ideal for beginners, seasoned data scientists looking to start with RNNs, business analysts, and those wanting to implement RNNs in projects. Through engaging exercises, carefully designed modules, and realistic RNN implementations, you will master RNNs, gain an overview of deep neural networks, understand RNN architectures, and perform text classification using TensorFlow.

Applied Learning Project

Learners will work on projects like creating an automatic book writer and a stock price prediction application, applying their RNN, LSTM, and TensorFlow skills to solve real-world problems and build practical, impactful solutions. Through these projects, they will gain hands-on experience in data preparation, model training, and evaluation, equipping them with the confidence to implement RNNs in diverse applications.