Bundles

Master the science behind Explainable AI. This complete two-volume series explores causal inference, Shapley values, attribution theory, fairness proofs, interpretability metrics, transformer explainability, and trustworthy AI. Learn the mathematical foundations that make modern AI systems transparent, accountable, and understandable.

Master the science of intelligent decision-making. This complete two-volume series covers utility theory, probabilistic reasoning, AI planning algorithms, Markov Decision Processes, Bayesian decision models, game theory, and reinforcement learning foundations. Learn how autonomous systems, robots, and modern AI agents make optimal decisions under uncertainty.

Master the mathematics behind modern artificial intelligence. This complete two-volume series takes you from Bellman equations and Markov Decision Processes to Q-Learning, Deep Q Networks, Policy Gradients, Actor-Critic architectures, and advanced reinforcement learning research. Perfect for students, researchers, and AI professionals seeking both theoretical depth and practical understanding.

Discover how ChatGPT-like systems are built from the ground up. This complete two-volume series teaches conversational AI, NLP, machine learning, transformers, LLMs, LangChain, RAG, chatbot deployment, and ethical AI. Learn to design, train, fine-tune, and deploy intelligent chatbots using the same core technologies powering modern AI assistants.

Discover the mathematical foundations behind intelligent machines. This complete two-volume series combines robotics, control systems, machine learning, and artificial intelligence into a unified framework for designing and analyzing modern autonomous systems. From kinematics and dynamics to reinforcement learning, SLAM, and AI-based control, this bundle provides the knowledge needed to build the next generation of intelligent robots.

Two practical books for teams building enterprise AI agents in production. Start with Claude Code, tools, MCP, evals, observability, and production agent workflows. Then secure those agents with bounded autonomy, AgentSecOps, RAG governance, identity controls, audit evidence, and regulatory readiness.

Most LLM books pick a side. Either they explain the math without showing the code, or they show the code without explaining the math. This is both. Two volumes. Eighteen chapters. From your first dot product in Chapter 5 to your fourth fine-tuning project in Chapter 17. Volume I builds the transformer from scratch. Volume II takes it into production. Together they are the only LLM set that walks the full arc from "what is attention" to "ship a fine-tuned model on a budget." If you have ever found a prompt trick that just worked and wished you knew why, or stared at a fine-tuning bill and wondered if you were doing it wrong, this is the set.

This GenAI bundle is perfect for ML engineers, AI product developers, and architects designing next-generation intelligent applications.

Capire la metrica giusta, usarla per creare rapporti causali con gli obiettivi prefissati. Imparare ad usare gli obiettivi come strumento di innovazione, miglioramento e monitoraggio dei propri cambiamenti. The right way è una collana di libro-racconto che illustrano diversi aspetti del lavoro di designer dei processi fortemente ispirata a The Goal e Radical Focus.

Yes, AI can generate outstanding books now. Yes, you can use it to create a passive income stream on the side or even as your main business. It’s not easy. No business system is. But publishing quality books is now very inexpensive, low-risk to try, and one of the most passive business models I can think of. Your books can literally sell themselves. No customer service. No major maintenance or inventory. You do not even need to use your real name. Self-publishing can be the perfect online business model, if it works.

Top to bottom system design like you have never seen it before. The ACM Code of Ethics taken head on.

We have forgotten constraint-based design because we now hide High-Performance Computing system constraints behind abstractions and inside infrastructure. But the techniques we developed and proved across the decades remain important for AI.

Cray Research was unusual in that we accomplished, repeatedly, what nobody else on the planet was able to accomplish. We never wrote down how we did it, until now.