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The Ultimate AI & Computer Science Encyclopedia: Complete Guide to AI, Machine Learning, Deep Learning, LLMs, MLOps, and AI Engineering - From Foundat
Paperback
Series: Modern Cloud & AI Engineering
Publisher: Independently Published
Published: Apr 12 2026
Pages: 312
Weight: 0.92
Height: 0.65 Width: 6.00 Depth: 9.00
Language: English
Tired of juggling twenty books, three courses, and hundreds of blog posts just to understand one concept? This encyclopedia ends that. Over 1,000 pages - from computer science foundations to deploying production LLM systems - organized so every concept buil2026ds on the last.
- 49 Major Topics with complete coverage: CS Foundations, Algorithms, Systems Design, Databases, Cloud/DevOps, Machine Learning, Deep Learning, Generative AI, LLMs, MLOps, Cybersecurity, AI Safety, and Future Technologies
- 335+ Reference Tables comparing algorithms, architectures, cloud services, fairness metrics, and optimization strategies side by side
- 133 Production-Grade Code Examples in Python - DQN, PPO, RAG pipelines, DDP training, LLM guardrails, A/B test calculators, and more
- Real-World Case Studies from Netflix, Uber, Google, Meta, and OpenAI
- Practice Problems with Full Solutions for technical interviews at top AI companies
Foundations: Boolean logic, Turing machines, P vs NP, CUDA execution model, GPU Tensor Cores, cache timing attacks, JIT compilation, torch.compile
ML & Deep Learning: Backpropagation derivation, ResNets, transformers, FlashAttention, GQA, RoPE, SwiGLU, knowledge distillation, contrastive learning, MAE, Mamba, state space models, NAS
Generative AI & LLMs: GPT/BERT/LLaMA/Mistral/Claude architectures, RLHF, Constitutional AI, DPO, RAG, vector databases, LoRA, QLoRA, AWQ quantization, vLLM, speculative decoding, prompt engineering, agents, tool use
MLOps & Systems: Feature stores, model monitoring, drift detection, A/B testing, Kubeflow, SageMaker, Vertex AI, Kubernetes GPU scheduling, KEDA autoscaling, canary deployment, SLOs, error budgets
Reinforcement Learning: MDPs, Q-learning, DQN, PPO, SAC, RLHF reward models, reward hacking, offline RL, Decision Transformer, multi-agent RL
Mathematics: Matrix calculus, Jacobians, convex optimization, KKT conditions, Gaussian processes, information theory, concentration inequalities, PAC learning
Cloud & DevOps: AWS CDK, Vertex AI Pipelines, Pulumi multi-cloud, spot instance training, FinOps, Docker, Terraform, CI/CD for ML
AI Ethics & Governance: EU AI Act, NIST AI RMF, algorithmic fairness, disparate impact, differential privacy, federated learning, AI incident response
Who This Is ForML engineers, data scientists, software engineers transitioning into AI, AI researchers, FAANG interview candidates, and graduate students in CS, Data Science, and Electrical Engineering.
Why This Stands ApartEvery topic includes: plain-language explanation, mathematical foundations, production Python code, real-world applications, and comparison tables. No filler. No padding. This is the reference we wish existed when we started building AI systems.
2026 Edition - covers LLaMA-3, Claude 3.5, GPT-4o, Mistral, Mamba, DeepSeek-V3, AWS Bedrock, and Vertex AI.
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