Recurrent Neural Networks, Vanilla and Gated - LSTM

Recurrent Neural Networks, Vanilla and Gated - LSTM

Explore recurrent neural networks, including vanilla and gated (LSTM) architectures, in this comprehensive lecture. Dive into various sequence processing techniques such as vector-to-sequence, sequence-to-vector, and sequence-to-sequence models. Learn about backpropagation through time, language modeling, and the challenges of vanishing and exploding gradients. Discover the Long Short-Term Memory (LSTM) architecture and its gating mechanism. Gain hands-on experience with a practical demonstration using Jupyter Notebook and PyTorch for sequence classification. Understand how to summarize research papers effectively and grasp the importance of higher hidden dimensions in neural networks.