0PP
Your ultimate resource for mastering AI/ML. This curated collection of questions is sourced from real interviews to help you prepare effectively.
the basics of a Recurrent Neural Network (RNN)
Classification Metrics (Accuracy, Precision, Recall, F1-Score)
Regularization techniques (L1, L2, Dropout)
the Generative Adversarial Network (GAN) architecture
the Support Vector Machine (SVM) algorithm and the kernel trick
Overfitting and Underfitting in models
the Gradient Descent algorithm
Transfer Learning and Fine-Tuning
the purpose of Batch Normalization
the TF-IDF vectorization technique
the role of a Feature Store
the BERT model and its significance
the purpose of Cross-Validation
Decision Trees and concepts like Entropy or Gini Impurity
Evaluation Metrics for NLP (e.g., BLEU, ROUGE)
the basics of a Recurrent Neural Network (RNN)
Classification Metrics (Accuracy, Precision, Recall, F1-Score)
Regularization techniques (L1, L2, Dropout)
the Generative Adversarial Network (GAN) architecture
the Support Vector Machine (SVM) algorithm and the kernel trick
Overfitting and Underfitting in models
the Gradient Descent algorithm
Transfer Learning and Fine-Tuning
the purpose of Batch Normalization
the TF-IDF vectorization technique
the role of a Feature Store
the BERT model and its significance
the purpose of Cross-Validation
Decision Trees and concepts like Entropy or Gini Impurity
Evaluation Metrics for NLP (e.g., BLEU, ROUGE)