20+ OpenAI Interview Questions And Answers

Transfer learning applies knowledge from one problem domain to another, improving learning efficiency and requiring less data for new tasks.

Attention mechanisms help NLP models focus on relevant parts of the input sequence, improving context understanding and model performance.

GANs consist of two neural networks, a generator and a discriminator, that compete against each other to generate new, synthetic instances of data.

RNNs (Recurrent Neural Networks) handle sequential data with internal memory, while CNNs (Convolutional Neural Networks) are effective for spatial data like images.

They map inputs to outputs in tasks like translation, where an encoder processes an input sequence and a decoder generates the target sequence.

Techniques include oversampling the minority class, undersampling the majority, or applying synthetic data generation methods.

Meta-learning is about designing models that learn how to learn, enabling quick adaptation to new tasks with minimal data.

Models like Isolation Forest or Autoencoders can be used, relying on their distinct ways to flag data points that deviate from the norm.

It's the trade-off between exploring novel actions for potential rewards and exploiting known actions that already give high rewards.

Hyperparameters, set prior to training, control the network's structure and learning process, affecting performance and convergence.

NLU enables machines to comprehend and interpret human language in context, forming the basis for tasks like question answering.

Metrics include accuracy, precision, recall, F1 score, and AUC-ROC, which assess different aspects of model performance.

LSTM (Long Short-Term Memory) networks avoid vanishing gradient problems by using gates to regulate information flow.

It normalizes the input layer by adjusting and scaling activations, stabilizing the learning process and speeding up convergence.

Embeddings are vector representations of words or phrases, capturing semantic relationships and enabling processing by machine learning models.

By designing models with explainability in mind, using model-agnostic interpretation tools, and including domain experts in the loop.

It occurs when gradients diminish as they propagate backwards, causing training to stall. Solutions include using LSTM networks or gradient clipping.

It's training a model on multiple tasks simultaneously, improving generalization by leveraging shared representations.

Dropout prevents overfitting by randomly disabling neurons during training, forcing the network to learn redundant representations.

P-hacking involves trying multiple statistical tests to find significant results by chance, undermining the validity of findings.