Deep Learning Interviews

Information Theory

Entropy, mutual information, KL divergence, and their applications in machine learning and neural networks.

Calculus & Algorithmic Differentiation

Chain rule, backpropagation, automatic differentiation, and computational graph optimization techniques.

Bayesian Deep Learning

Probabilistic programming, uncertainty quantification, variational inference, and Bayesian neural networks.

Logistic Regression

Maximum likelihood estimation, regularization, feature engineering, and classification fundamentals.

Ensemble Learning

Bagging, boosting, random forests, gradient boosting, and model combination strategies.

Feature Extraction

Dimensionality reduction, PCA, t-SNE, feature selection, and representation learning techniques.

Shlomo Kashani

Lead Author & AI Researcher

Shlomo brings extensive experience in deep learning research and education. His passion for making complex AI concepts accessible to students and professionals drives the pedagogical approach of this comprehensive guide.

Amir Ivry

Co-Author & Technical Contributor

Amir contributes deep technical expertise and practical insights from industry experience. His collaboration ensures the book addresses real-world interview scenarios and current industry practices.