04.2 – Recurrent neural networks, vanilla and gated (LSTM)

About this Video

• Video Title: 04.2 – Recurrent neural networks, vanilla and gated (LSTM)
• Channel: Alfredo Canziani (冷在)
• Speakers: Alfredo Canziani
• Duration: 01:05:36

Introduction

This video lecture covers recurrent neural networks (RNNs), focusing on vanilla RNNs and gated RNNs, specifically LSTMs. The instructor explains the architecture, training (backpropagation through time), and applications of RNNs for sequential data processing. He also addresses vanishing/exploding gradients and demonstrates a sequence classification example using PyTorch.

Key Takeaways

• Note-Taking for Papers: The lecturer shares a method for summarizing research papers using Notion, emphasizing a structured approach with sections for "Why," "What," "How," TL;DR, code implementation, formulas, and final notes.
• High-Dimensional Spaces in Neural Networks: The use of higher-dimensional hidden layers is explained through animations, demonstrating how lower dimensions lead to brittle and difficult-to-train models, while higher dimensions facilitate smoother transformations.
• Recurrent Neural Network Architectures: Different RNN applications are categorized: vector-to-sequence, sequence-to-vector, and sequence-to-vector-to-sequence. Examples include image captioning (vector-to-sequence) and program execution prediction (sequence-to-vector).
• Backpropagation Through Time: The training of RNNs is explained as backpropagation through time, emphasizing parameter sharing and the accumulation of gradients across time steps. The concept of vanishing/exploding gradients is introduced, and LSTMs are presented as a solution.
• LSTMs (Long Short-Term Memory): LSTMs are detailed as gated RNNs using input, remember (forget), and output gates to control the flow of information, mitigating vanishing/exploding gradients. A PyTorch implementation of LSTM for sequence classification is demonstrated.