100 Days of Deep Learning - LSTM Architecture

Introduction

• A high-level introduction to LSTM was given in the previous video.
• In today's video, we will understand the architecture of LSTM in detail.
• The video will be long, please be patient.

Architecture of LSTM

• Comparative discussion of LSTM and RNN architectures.
• Ability of LSTM to maintain long-term and short-term memory.

Key Features of LSTM

1. Long-term Memory
   • Represented through cell states.
2. Short-term Memory
   • Represented through hidden states.
3. Interaction
   • Facilitates interaction between long-term and short-term memory.

Main Components of LSTM Architecture

1. Forget Gate

   • The function of this gate is to remove unnecessary information from long-term memory.
   • The input consists of three things: previous cell state, previous hidden state, and current input.

2. Input Gate

   • Adds new important information to long-term memory.
   • Works based on candidate cell state and input.

3. Output Gate

   • Decides the output for the current time step.
   • Extracts the current hidden state from long-term context.

Operation and Mathematical Modeling

• Use of pointwise multiplication and addition.
• Consistent number of units across all gates.

In-depth Understanding of Gates

• The working method of each gate and their mathematical model.
• Coordination between long-term memory and short-term memory with the help of gates.

Conclusion

• Complete understanding of LSTM architecture and its utility.
• Mention of animation in the video, which clearly demonstrates the functioning of LSTMs.

These notes are for a complete understanding of the LSTM architecture and the next video will discuss its practical application.