Data Science and Machine Learning (Theory and Projects) A to Z - Vanishing Gradients in RNN: LSTM Optional

The video tutorial explains Long Short-Term Memory (LSTM) networks with a focus on mathematical equations rather than pictorial representations. It covers the definition and role of C hat, the candidate memory cell, and the various gates in LSTM, including update, forget, and output gates. The tutorial also discusses the parameters involved in LSTM and compares them with Gated Recurrent Units (GRU), highlighting LSTM's flexibility and complexity. Additionally, it introduces the peephole connection, a variant of LSTM that uses previous C values, and concludes with a note on standard implementations.

Why might some learners prefer mathematical explanations over pictorial ones when studying LSTM?

• Mathematical explanations are more colorful.

• Equations provide a deeper understanding for some learners.

• Pictures are too complex to understand.

• Mathematical explanations are faster to read.

What is the primary function of the tanh activation in LSTM?

• To saturate quickly.

• To reduce the number of parameters.

• To explode quickly.

• To capture memory longer than sigmoid or ReLU.

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

• Output gate

• Input gate

• Forget gate

• Update gate

How is the cell state (C) in LSTM updated?

• By adding new information and forgetting old information.

• By only adding new information.

• By only forgetting old information.

• By copying the previous cell state.

What is a peephole connection in the context of LSTM?

• A variant that uses previous cell states in gate computations.

• A technique to visualize LSTM operations.

• A connection that allows direct access to the output gate.

• A method to reduce the number of parameters.

Why are LSTMs considered more flexible than GRUs?

• They have fewer parameters.

• They use only sigmoid activations.

• They have more parameters and can model complex patterns.

• They are simpler to implement.

What is the common approach when choosing between LSTM variants for different datasets?

• Choose the variant with the least parameters.

• Always use the variant without peephole connections.

• Always use peephole connections.

• Use standard implementations as they work well generally.