Data Science and Machine Learning (Theory and Projects) A to Z - Sentiment Classification using RNN: What Next

The video discusses the implementation of recurrent neural networks (RNNs) from scratch, highlighting the limitations of simple RNNs in handling long-term dependencies due to vanishing and exploding gradients. It introduces advanced RNN architectures like LSTMs, GRUs, and attention mechanisms that address these issues. The video also outlines upcoming projects, including an automatic Shakespeare writer and stock prediction model, both implemented in TensorFlow. The benefits of using TensorFlow's high-level APIs for shorter and more efficient code are emphasized.

What are the limitations of simple recurrent neural network cells in handling long-term dependencies?

Explain the concept of vanishing gradients and how it affects the training of recurrent neural networks.

What are LSTM and GRU, and how do they address the issues faced by traditional RNNs?

Describe the role of attention mechanisms in advanced neural network architectures.

What advantages do high-level APIs in TensorFlow provide when coding neural networks?