This video tutorial is the third course in a Tensorflow series, focusing on recurrent neural networks (RNNs). It begins with an introduction to neural networks and their components, followed by a detailed exploration of RNNs, their theory, and practical applications. The course covers building RNNs using Tensorflow, designing RNN architectures, and applying them to real-world problems like time series forecasting, text classification, and image recognition. The instructor also addresses misconceptions about stock predictions with LSTMs and motivates learners to advance their careers through this course.

What are the best practices for building RNNs today as discussed in the course?

How do modern RNN units like GRU and LSTM enhance the capabilities of neural networks?