Interview Questions and Answers

The vanishing gradient problem occurs when gradients become extremely small during backpropagation, leading to negligible weight updates in early layers. This hinders the training of deep networks, as early layers fail to learn meaningful representations.

Batch normalization normalizes the inputs of a layer within a mini-batch, reducing internal covariate shift. It stabilizes and accelerates the training process, enables the use of higher learning rates, and acts as a form of regularization, reducing the reliance on techniques like dropout.

LSTMs are a type of RNN designed to address the vanishing gradient problem. They use memory cells and gates to selectively store and retrieve information over long sequences, making them more effective at capturing long-range dependencies in data.

A GAN consists of a generator and a discriminator trained simultaneously through adversarial training. The generator generates synthetic data, while the discriminator distinguishes between real and fake data. GANs are used for image generation, style transfer, and data augmentation.

The curse of dimensionality refers to the challenges and increased complexity that arise when dealing with high-dimensional data. As the number of features or dimensions increases, the amount of data required to cover the space adequately grows exponentially. This can lead to issues such as sparsity and increased computational requirements.

Attention mechanisms allow a model to focus on specific parts of the input sequence when making predictions. They are commonly used in natural language processing tasks, such as machine translation, where the model needs to selectively attend to relevant words or tokens in the input.