Bundles

The ultimate guide to cracking Android technical interviews and a dissection of what runs beneath every @Composable.

From your first LLM call to production-grade AI systems. Seven books covering agents, MCP, prompt engineering, evals, cost optimization, and observability — the complete engineering stack for building with AI in 2026. Save over 40% versus buying individually.

Everything is connected.People, computers, businesses, cities, knowledge, and intelligent systems all exist within networks of relationships.Understanding those relationships is the key to modern Artificial Intelligence.The Graph Theory with AI Applications Complete Series (VOL-1 & VOL-2) takes readers from classical graph theory foundations to cutting-edge Graph Neural Networks and Graph AI.Inside this bundle, you'll discover:✓ Graph Theory Fundamentals✓ Graph Traversal and Pathfinding Algorithms✓ Shortest Path and Network Optimization✓ Social Network Analysis✓ Community Detection and Graph Mining✓ Graph Representation Learning✓ Graph Embeddings (DeepWalk, Node2Vec, LINE)✓ Graph Neural Networks (GCN, GAT, GraphSAGE)✓ Knowledge Graphs and Graph-Based NLP✓ AI-Powered Recommendation Systems✓ Cybersecurity and Fraud Detection✓ Explainable Graph AI✓ Distributed Graph Learning✓ Graph Foundation Models✓ Quantum Graph Neural NetworksWhether you are a student, researcher, educator, or AI professional, this bundle provides the mathematical foundations and modern AI techniques required to understand how intelligent systems learn from connected data.Learn Graph Theory.Master Graph Neural Networks.Build the future of connected intelligence.

What is intelligence?At its core, intelligence is the ability to acquire, compress, transform, communicate, and generate information.The Information Theory and Artificial Intelligence Complete Series explores the mathematical foundations that power modern machine learning, deep learning, generative AI, reinforcement learning, and large language models.Inside this two-volume series, you'll discover:✓ Shannon Entropy and Information Measures✓ Mutual Information and Representation Learning✓ Cross-Entropy and KL Divergence✓ Data Compression and Coding Theory✓ Information Bottleneck Theory✓ Variational Autoencoders (VAEs)✓ Contrastive and Self-Supervised Learning✓ Generative Adversarial Networks (GANs)✓ Entropy-Driven Reinforcement Learning✓ Fisher Information and Information Geometry✓ Information Theory Behind Transformers and LLMs✓ Federated Learning and Distributed Intelligence✓ Explainable AI Through Entropy✓ Quantum Information Theory✓ AI Fairness, Safety, Alignment, and Future ResearchWhether you are learning information theory for the first time or exploring the mathematical foundations of advanced AI systems, this bundle provides the tools, theory, and insights needed to understand how information becomes intelligence.Learn not just how AI works—but why it works.

What happens when intelligent machines must compete, cooperate, negotiate, and learn in the same environment?The answer lies at the intersection of Game Theory and Artificial Intelligence.This complete two-volume series explores how modern AI systems make strategic decisions in environments filled with uncertainty, competition, cooperation, and dynamic interactions.Inside the bundle, you'll discover:✓ Classical Game Theory and Nash Equilibrium✓ Multi-Agent Systems and Distributed Intelligence✓ Reinforcement Learning and Multi-Agent RL✓ Deep Reinforcement Learning Algorithms✓ Nash Q-Learning, Minimax-Q, and Correlated-Q✓ DQN, Actor-Critic, MADDPG, QMIX, and CTDE✓ Mechanism Design and Incentive Engineering✓ Decision Making Under Uncertainty✓ Social Choice Theory and Collective Intelligence✓ Applications in Robotics, Economics, Networks, and Cybersecurity✓ Evolutionary and Quantum Game Theory✓ AI Safety, Governance, and Alignment✓ Future Research Directions in Strategic AIFrom autonomous vehicles and intelligent robots to cybersecurity defense systems and large-scale AI simulations, this bundle provides the mathematical foundations, algorithmic techniques, and practical insights needed to understand the future of intelligent autonomous systems.Whether you are a student, researcher, engineer, educator, or AI professional, this series offers a comprehensive roadmap to mastering strategic artificial intelligence and multi-agent intelligence.

The complete ECBA® prep toolkit — study guide, 555-question bank, and 4 full mock exams. 755+ practice questions, full rationales, near-miss analysis. Save $20 vs. buying separately.

755 ECBA® practice questions — 4 full mock exams plus a 555-question domain bank. Near-miss analysis on every answer. Save 26% vs. buying separately.

200 ECBA® practice questions across 4 full mock exams — near-miss analysis on every answer, Blueprint V1.1 aligned. Save 22% vs. buying separately.

Why learn to touch-type Chinese, or failing that, why learn this shape-based input system? Because it is one way of practicing a key element of learning, breaking things down into clearly defined (and recognizable) elements. Plus, it's really cool to touch-type Chinese (Traditional Chinese) characters.

How do machines predict future outcomes? Why do some models generalize well while others fail? How can AI systems estimate uncertainty, explain their predictions, and operate reliably in real-world environments?Linear and Nonlinear Regression in Artificial Intelligence: Mathematical Foundations, Regularization Techniques & Predictive Modeling (Complete Bundle Edition) provides a comprehensive exploration of predictive intelligence—from classical linear regression to advanced Bayesian models, Gaussian Processes, neural network regression, kernel methods, explainable AI, and large-scale machine learning systems.Combining mathematical rigor, practical machine learning techniques, real-world case studies, and modern AI applications, this two-volume collection equips readers with the knowledge required to design, evaluate, interpret, and deploy predictive models across healthcare, finance, engineering, business analytics, scientific research, and next-generation AI systems.Whether you are a student, researcher, data scientist, or AI professional, this bundle offers a complete roadmap to mastering the science of prediction.

earning agents learn through exploration? How do generative AI systems create realistic content? And why is randomness one of the most powerful ingredients of intelligence itself?Stochastic Processes in Artificial Intelligence: Foundations, Algorithms, and Applications (Complete Bundle Edition) provides a comprehensive journey through the mathematics, algorithms, and real-world applications of probabilistic AI.From probability theory, Markov Chains, Hidden Markov Models, stochastic optimization, and reinforcement learning to Bayesian deep learning, probabilistic graphical models, Kalman filtering, robotics, generative AI, stochastic differential equations, and future research directions, this two-volume collection reveals how uncertainty drives intelligent behavior.Combining rigorous mathematics, intuitive explanations, practical algorithms, case studies, and cutting-edge AI applications, this bundle is an essential resource for students, researchers, engineers, and professionals seeking mastery of probabilistic intelligence and modern AI systems.