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DSE - Data Science Deep Learning and Neural Networks Flashcards

6 cards from real DSE practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

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  1. Which neural network architecture processes sequential data by maintaining a hidden state that captures information from previous time steps?

    Answer: Recurrent Neural Network (RNN)

    RNNs have recurrent connections that pass the hidden state from one time step to the next, enabling them to model temporal dependencies in sequential data like text or time series.

  2. What is transfer learning in deep learning?

    Answer: Reusing a model pretrained on one task as the starting point for training on a related task

    Transfer learning leverages features learned by a model on a large source task (e.g., ImageNet) and fine-tunes it on a smaller target task, dramatically reducing data and compute requirements.

  3. What does batch normalization do in a neural network?

    Answer: Normalizes the inputs to each layer to have zero mean and unit variance within each mini-batch

    Batch normalization standardizes layer inputs within each mini-batch, stabilizing training, reducing sensitivity to weight initialization, and allowing higher learning rates.

  4. What is the primary function of a pooling layer in a Convolutional Neural Network?

    Answer: Downsampling feature maps to reduce spatial dimensions and computation

    Pooling layers (e.g., max pooling) reduce the height and width of feature maps by aggregating values in local regions, decreasing computation and providing some translation invariance.

  5. What is an autoencoder primarily used for in deep learning?

    Answer: Unsupervised dimensionality reduction and feature learning by learning to compress and reconstruct data

    An autoencoder trains an encoder to compress input into a lower-dimensional latent representation and a decoder to reconstruct the original input, learning compact data representations without labels.

  6. Which loss function is typically used for binary classification problems in neural networks?

    Answer: Binary cross-entropy

    Binary cross-entropy measures the dissimilarity between the predicted probability and the true binary label, penalizing confident wrong predictions most heavily.