Rapid Fire on Artificial Intelligence

LSTM: Long Short-term Memory GRU: Gated Recurrent Unit End-to-end Network Memory Network In an interview setting, when asked about the variants of Recurrent Neural Networks (RNNs), you should mention several key variants that have been developed to address different challenges and improve performance. Here are some important variants of RNNs: Vanilla RNNs: The basic form … Read more

Gates (forget, Memory, update, and Read) Tanh(x) (values between −1 and 1) Sigmoid(x) (values between 0 and 1) In an interview setting, providing a comprehensive answer to the question about the key components of Long Short-Term Memory (LSTM) networks would demonstrate your understanding of this fundamental architecture in artificial intelligence and recurrent neural networks. Here’s … Read more

Long short-term memory (LSTM) is explicitly designed to address the long-term dependency problem, by maintaining a state of what to remember and what to forget. For an artificial intelligence interview question asking to define LSTM (Long Short-Term Memory), the correct answer would be: “LSTM stands for Long Short-Term Memory, which is a type of recurrent … Read more

As we add more and more hidden layers, backpropagation becomes less useful in passing information to the lower layers. In effect, as information is passed back, the gradients begin to vanish and become small relative to the weights of the network. The vanishing gradient problem refers to the issue encountered in training deep neural networks … Read more

Learning rate: The learning rate is how fast the network learns its parameters. Momentum: It is a parameter that helps to come out of the local minima and smoothen the jumps while gradient descent. Number of epochs: The number of times the entire training data is fed to the network while training is referred to … Read more