Fundamentals of Neural Networks - Long Short-Term Memory (LSTM)

The video tutorial introduces Long Short Term Memory (LSTM) architecture, a key component in modern recurrent neural networks. It explains the basic structure and function of LSTM, including its gates: forget, update, and output. The tutorial delves into the mathematical formulation of these gates and how they contribute to the LSTM's ability to retain information over time. It also covers how LSTM units produce outputs and make predictions using softmax functions. Finally, the video discusses constructing deep LSTM layers for complex neural network architectures.

What is the primary use of LSTM in modern neural networks?

• Game development

• Speech synthesis

• Stock data prediction

• Image recognition

Which gate in the LSTM architecture is responsible for deciding what information to discard from the cell state?

• Forget gate

• Output gate

• Update gate

• Input gate

What type of function is used in the LSTM's update gate?

• ReLU

• Sigmoid

• Softmax

• Tanh

In the LSTM mathematical formulation, what does the star symbol (*) represent?

• Element-wise multiplication

• Matrix multiplication

• Addition

• Subtraction

What is the purpose of the C~ in the LSTM cell?

• To calculate the bias

• To store the final output

• To initialize the forget gate

• To represent the candidate value for the cell state

How does the LSTM architecture handle long sequences of data?

• By increasing the learning rate

• By stacking multiple LSTM units

• By reducing the number of gates

• By using a single layer

What is the main advantage of using deep LSTM layers in neural networks?

• Faster computation

• Ability to memorize long sequences

• Reduced complexity

• Lower memory usage