Authors
Meet the Authors
Experienced researchers and educators dedicated to advancing AI education.
Hundreds of Fully Solved Job Interview Questions from Key AI Topics
Written by Shlomo Kashani, this comprehensive guide provides step-by-step solutions, clear diagrams, and detailed reasoning to make complex concepts intuitive for aspiring data scientists and AI professionals.
See the quality of explanations and comprehensive content firsthand.
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A comprehensive curriculum covering the mathematical and computational foundations of deep learning.
Entropy, mutual information, KL divergence, and their applications in machine learning and neural networks.
Chain rule, backpropagation, automatic differentiation, and computational graph optimization techniques.
Probabilistic programming, uncertainty quantification, variational inference, and Bayesian neural networks.
Maximum likelihood estimation, regularization, feature engineering, and classification fundamentals.
Bagging, boosting, random forests, gradient boosting, and model combination strategies.
Dimensionality reduction, PCA, t-SNE, feature selection, and representation learning techniques.
Comprehensive treatment covering neural networks, CNNs, RNNs, attention mechanisms, and modern architectures with PyTorch, Python, and C++ examples.
Extended Chapter (100+ pages)Experienced researchers and educators dedicated to advancing AI education.
Available in multiple formats — choose what works best for you.
Questions about the book? solomon@qneura.ai