Welcome to this insightful discussion on the challenges faced by simple recurrent neural networks (RNNs). In this video, we will explore the problems associated with these networks and gain an understanding of why they may not be the optimal choice for certain applications.
Contents
The Vanishing and Exploding Gradient Problem
When performing backward propagation in time, the derivative of the weights in a simple RNN is continuously updated with respect to time. However, there are two significant problems that arise during this process – the vanishing gradient problem and the exploding gradient problem.
The Vanishing Gradient Problem
Let’s delve into the vanishing gradient problem. Consider the hidden layer of neurons within the RNN that employ activation functions such as the sigmoid or ReLU. The derivative of the sigmoid function always falls within the range of 0 to 1. As we traverse through the weights towards the end, the derivative becomes extremely small due to the continuous updates. This diminishing value renders the weight updates negligible. Consequently, the RNN fails to converge to the global minimum point of gradient descent. This phenomenon is known as the vanishing gradient problem.
The Exploding Gradient Problem
Conversely, the exploding gradient problem occurs when using activation functions with derivatives greater than one. As the updates occur, the changes in weights become substantial, often leading to a divergence from the global minimum point. The resulting effect is an RNN that continually rotates within a certain range without converging to the desired destination.
Long Short-Term Memory (LSTM) Networks
To address these issues, a more sophisticated architecture called long short-term memory (LSTM) networks were developed. These networks differ from simple RNNs in their ability to mitigate the vanishing and exploding gradient problems. In future videos, we will explore the architectural differences between these two types of networks, shedding further light on the superiority of LSTM networks in handling recurrent dependency challenges.
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