[Deep Learning 101] Step-by-Step LSTM: The Ultimate Guide to LSTM from Scratch

Summary

This video introduces Long Short-Term Memory (LSTM) networks, explaining how they address the long-term dependency problem in Recurrent Neural Networks (RNN). It covers the background of LSTM's creation, its structure, and key concepts, including gates like the forget gate, input gate, and output gate. The video breaks down how LSTM processes and updates the cell state to retain relevant information. A deep dive into the mathematical computations of LSTM is also provided, alongside an overview of backpropagation through time (BPTT) and gradient descent for training. The tutorial concludes by emphasizing the efficiency of LSTM in handling time-series data.

Takeaways

• 😀 LSTM (Long Short-Term Memory) is an advanced neural network designed to overcome the long-term dependency problem found in traditional RNNs.
• 😀 Unlike RNNs, which struggle to maintain information over time, LSTM uses a specialized architecture with gates that can retain and forget information at each time step.
• 😀 LSTM consists of four key components: the forget gate, input gate, candidate gate, and output gate, each serving a distinct purpose in handling time series data.
• 😀 The forget gate controls what information from the past (previous cell state) should be discarded, using a sigmoid function to output values between 0 and 1.
• 😀 The input gate decides what new information should be added to the cell state, in conjunction with the candidate gate, which normalizes the input using a tanh function.
• 😀 The output gate regulates how much of the updated cell state should be exposed to the next layer in the neural network, producing the hidden state (HST).
• 😀 LSTM’s cell state (CST) stores long-term memory, while the hidden state (HST) contains short-term information based on the current input.
• 😀 LSTM processes inputs by performing element-wise operations to update memory, and each gate works with specific activation functions (sigmoid and tanh) to control the flow of information.
• 😀 LSTM handles the vanishing gradient problem better than RNN by using an additive backpropagation mechanism, which prevents gradients from diminishing over time.
• 😀 Backpropagation through time (BPTT) in LSTM involves updating the weights for each gate based on the gradient of the loss function, ensuring the network learns efficiently over time.

Q & A

What is LSTM and why was it developed?

LSTM stands for Long Short-Term Memory, a type of neural network designed to process time-series data. It was developed to overcome the long-term dependency problem that RNNs face, where the model struggles to retain information over long sequences.

What is the main weakness of RNN that LSTM aims to solve?

RNNs suffer from the long-term dependency problem, where the gradient diminishes as the sequence length increases. This makes it difficult for the model to learn from distant parts of the sequence, leading to poor performance on tasks like machine translation.

What role does the 'cell state' play in LSTM?

The cell state is the key to overcoming long-term dependency issues in LSTM. It acts as a memory bank that carries information across time steps and can be updated through gates to remember or forget specific details.

How does the forget gate work in LSTM?

The forget gate decides what information should be forgotten from the cell state. It takes in the concatenated previous hidden state and current input, processes it through a sigmoid function, and multiplies the result element-wise with the cell state to retain or erase information.

What is the difference between the input gate and the forget gate in LSTM?

Both the input and forget gates use a sigmoid function, but their roles differ. The forget gate determines what should be forgotten from the cell state, while the input gate decides what new information should be added to the cell state, helping it update memory.

What is the candidate gate in LSTM and how does it function?

The candidate gate is responsible for creating potential new information to add to the cell state. It uses a hyperbolic tangent function to normalize incoming values between -1 and 1, and then multiplies this by the output of the input gate to decide what to remember.

How does LSTM update its hidden state?

LSTM updates its hidden state by combining the current cell state and the output gate's processed information. The new hidden state reflects short-term memory, while the cell state retains long-term information.

What is the role of the output gate in LSTM?

The output gate controls what part of the cell state should be output as the hidden state for the current time step. It uses the hyperbolic tangent function to normalize the cell state and then element-wise multiplies it to produce the hidden state.

What is the process of backpropagation through time (BPTT) in LSTM?

Backpropagation through time (BPTT) in LSTM involves computing the gradient of the loss with respect to each weight in the network, updating them using gradient descent. It works by calculating derivatives for each gate and weight, and adjusting the parameters based on the error.

Why does LSTM perform better than RNN when it comes to long-term dependencies?

LSTM handles long-term dependencies better than RNN because its use of the cell state and gates allows for controlled updates to memory. Unlike RNNs, which rely on continuous multiplication, LSTM's use of addition reduces the likelihood of gradients disappearing over time.