Direct links to the current book projects and publication pages.
Hundreds of fully solved job interview questions from key AI topics.
A practical machine learning and deep learning reference for candidates, researchers, and engineering teams.
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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)Available in multiple formats — choose what works best for you.
Questions about the book? solomon@qneura.ai