Deep Learning Interview Questions and Answers
Experienced / Expert level questions & answers
Ques 1. What is the vanishing gradient problem, and how does it affect deep neural networks?
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.
Ques 2. Explain the concept of batch normalization and its advantages in training deep neural networks.
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.
Ques 3. Explain the concept of Long Short-Term Memory (LSTM) networks and their advantages over traditional RNNs.
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.
Ques 4. Explain the concept of a generative adversarial network (GAN) and its applications.
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.
Ques 5. What is the curse of dimensionality, and how does it affect machine learning algorithms?
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.
Ques 6. Explain the concept of attention mechanisms in neural networks and their applications.
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.
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