RESOURCES

The definitive textbook on AI fundamentals, covering search algorithms, knowledge representation, machine learning, and robotics. Ideal for readers who want a deep theoretical grounding.

A practical, code-first guide to building real-world ML and deep-learning systems in Python. Includes end-to-end projects and downloadable notebooks.

Comprehensive treatment of deep neural networks: theory, architectures (CNNs, RNNs), optimization techniques, and recent advances. Suited to both students and practitioners.

Explores the future impact of AI on society, economy, and ethics. Raises essential questions about governance, safety, and the long-term trajectory of superintelligent systems.

An economic lens on AI—how dramatically cheaper prediction reshapes business strategy, labor markets, and value creation.

Investigates why AI systems sometimes go off-track and how researchers are tackling robustness, fairness, and interpretability to align algorithms with our values

A concise, practitioner-focused handbook on how to structure ML projects, diagnose errors, and iteratively improve model performance without diving into heavy math.

A visionary tour of the five “tribes” of machine learning—symbolists, connectionists, evolutionaries, Bayesians, and analogizers—and how their unification could yield a universal learning algorithm. Great for readers curious about the big ideas and future impact of AI.

An accessible, hands-on introduction that builds deep learning concepts from first principles. You’ll implement neural networks in pure Python as you learn, making complex ideas intuitive and practical.