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The Generative AI & LLM Engineering Bundle
Stop treating Large Language Models like black-box APIs.
Master the complete Generative AI engineering stack—from probability equations and neural networks built from scratch to vector semantic search and production-grade LLM system architecture. This 4-book curriculum equips developers and software architects with the first-principles intuition needed to build, scale, and innovate with confidence.
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The following 4 books are included in this bundle...
These books have a total suggested price of $53.97. Get them now for only $22.99!
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About the Bundle
The Generative AI & LLM Engineering Bundle
From Mathematical Foundations to Production-Grade AI SystemsStop treating Large Language Models like black-box APIs.
The Generative AI & LLM Engineering Bundle is a complete, 4-book curriculum engineered for developers, software architects, and ML practitioners who want to build, understand, and scale modern AI systems from first principles.
Instead of superficial tutorials, this bundle provides a seamless learning path: from the core equations and neural network code up to high-dimensional semantic search and production-grade system architecture.
📦 What’s Included in This Bundle?1. The Mathematics of Generative AI: From Probability to Language Models
Demystify the exact equations driving modern LLMs. Learn probability distributions, vector geometry, and scaled dot-product attention through clear intuition and code-first mappings.
2. Behind the Black Box: Building Neural Networks From Scratch
Understand deep learning by building it. Implement feedforward networks, backpropagation, and fundamental neural building blocks from scratch without relying on heavy frameworks.
3. Semantic Search From Scratch
Master vector embeddings, similarity metrics, and retrieval mechanisms. Learn how to architect fast, reliable semantic search engines and Retrieval-Augmented Generation (RAG) pipelines.
4. AI Systems Engineering
Bridge the gap from prototype to production. Learn how to architect, optimize, monitor, and deploy scalable LLM infrastructure capable of handling real-world enterprise traffic.
🎯 What You Will Learn & Build- Core Math to Code: Translate conditional probability, matrix transformations, and self-attention straight into Python.
- Framework-Free Neural Nets: Implement backpropagation and activation functions to see how representations are learned.
- Vector & RAG Architectures: Build vector indexing and semantic retrieval pipelines for production LLM context injection.
- Production-Ready Engineering: Master latency reduction, context caching, system resilience, and deployment patterns for AI backends.
- Backend & Systems Engineers moving into Generative AI and LLM infrastructure.
- Machine Learning Engineers who want to master system design and production engineering.
- Tech Leads & Architects designing enterprise RAG systems and LLM workflows.
Buying these books together gives you a complete, end-to-end curriculum—from theory to code to production deployment—at a discounted bundle price.
Master the mathematics. Build the algorithms. Scale the systems.
Books
About the Books
The Mathematics of Generative AI: From Probability to Language Models
A Complete Theoretical Foundation for Modern Generative Models
Book Overview
Generative AI can often feel like magic, but behind every token prediction, attention weight, and context window lies a clean foundation of applied mathematics. The Mathematics of Generative AI: From Probability to Language Models bridges the gap between abstract equations and modern engineering, providing developers and AI practitioners with a clear, code-first roadmap.
Instead of drowning in academic proofs, this capsule translates essential mathematical concepts—probability theory, high-dimensional vector spaces, and linear transformations—directly into practical intuition for building and understanding modern Large Language Models (LLMs).
Key Takeaways & Core Topics- Probability Foundations for LLMs: Master conditional probability, joint distributions, and sampling methods (Greedy, Top-k, Top-p) that govern autoregressive token generation.
- Vector Spaces & Embeddings: Understand how high-dimensional geometry represents semantic meaning, distance metrics (Cosine, Euclidean), and matrix factorization.
- The Linear Algebra of Attention: Deconstruct scaled dot-product attention, Query-Key-Value (Q, K, V) projections, and positional encodings step-by-step.
- From Equations to Python: Connect mathematical formulations directly to idiomatic Python code, showing how theory translates to execution.
- Software & AI Engineers who want to move beyond viewing LLMs as black-box APIs.
- Data Science & ML Students seeking an intuitive, applied bridge between university math and state-of-the-art Generative AI architectures.
- Technical Authors & Educators looking for a structured reference to explain complex AI mechanics clearly.
Semantic Search from Scratch
Build a Working Semantic Search Engine with Pure Python and NumPy
Modern AI systems retrieve information by meaning rather than exact keyword matches. Under the hood, this capability relies on a surprisingly simple foundation: representing text as vectors, measuring similarity, and ranking results.
In Semantic Search from Scratch, you will build this core mechanism yourself from first principles using only Python and NumPy.
We deliberately avoid third-party vector databases, machine-learning frameworks, and high-level retrieval libraries. The goal isn't to build a production platform, but to demystify the mathematical mechanics behind semantic retrieval, vector spaces, and Retrieval-Augmented Generation (RAG).
What You Will Learn & Build:
- Vectors as Representations: Understand how natural language transforms into dense numerical representations.
- Mathematical Similarity: Implement Cosine Similarity from scratch using raw matrix operations.
- Document Indexing: Construct a complete educational vector index to store and query text representations.
- Top-K Retrieval & Ranking: Build a scoring loop that ranks contextually relevant documents for downstream AI prompts.
- RAG Foundations: Connect the dots between vector similarity and modern AI retrieval pipelines.
Who Is This Capsule For?
This guide is designed for intermediate Python developers, AI engineers, and curiosity-driven builders who want to peel back the layers of high-level AI libraries and truly understand how semantic search works under the hood.
AI Systems Engineering
From Prototype to Production
Master the Transition from AI Prototypes to Production-Grade Systems
Moving artificial intelligence and generative models from experimental Jupyter notebooks to scalable, reliable production environments requires robust systems engineering. This book is a practical, hands-on guide for software engineers, machine learning practitioners, and technical architects building end-to-end AI infrastructure.
What You Will Learn:
• Foundations & Architecture: Deep dive into LLM architectures, Generative AI mechanisms, and core Machine Learning principles.
• Performance Optimization: Eliminate computational bottlenecks, optimize pure Python execution, and leverage high-performance vectorized operations.
• Production Deployment: Build secure, scalable containerized microservices and backend architectures for AI workloads.
• Real-World Implementation: Construct robust AI pipelines using modern Python frameworks and production best practices.
Whether you are an engineer looking to scale your AI prototypes or an architect designing enterprise systems, this book bridges the gap between machine learning concepts and production-grade deployment.
Behind the Black Box: Building Neural Networks From Scratch
The Essential 5-Function Guide to Mastering AI Mathematics with Pure Python and NumPy. Stop copying code—become the architect.
Have you ever felt like you're just "copying and pasting" AI code without really understanding what’s happening behind the screen? Most developers can write "model.fit()", but very few actually know the exact mathematics that execute the moment they hit enter. When the model fails, when accuracy drops, or when faced with unique unstructured data, they are left helpless. They are trapped inside the "Black Box" of high-level frameworks like TensorFlow and PyTorch. This book is your escape route. "Behind the Black Box" is a practical, line-by-line guide designed specifically for developers, students, and educators who want to master Deep Learning from the absolute foundations. No dry academic fluff, no intimidating textbook gatekeeping—just pure logic, elegant math, and raw Python code. In this book, you will deconstruct a Neural Network into just 5 core mathematical functions and build the entire engine from scratch using nothing but NumPy. -------------------------------------------------------------------- 💡 WHAT YOU WILL LEARN & BUILD: -------------------------------------------------------------------- * Function #1: The Linear Forward Step – Master matrix multiplication (Z = W.X + b) and structure data routing. * Function #2: The Sigmoid Activation – Build a probability filter to inject non-linear life into your network. * Function #3: The ReLU Activation – Code the speed king of modern AI and understand sparse firing. * Function #4: Binary Cross-Entropy – Create the network's reality check to measure its flaws exponentially. * Function #5: Gradient Descent & Backpropagation – Reverse-engineer errors using the Chain Rule to update weights. * The Grand Loop – Put all 5 functions together into a working, training, pattern-recognizing artificial brain. -------------------------------------------------------------------- 🎯 WHO IS THIS BOOK FOR? -------------------------------------------------------------------- * Aspiring AI Engineers who want a competitive edge in technical interviews. * Python Developers tired of blindly relying on heavy black-box libraries. * Mathematics Teachers and students looking for a clear, code-driven bridge to applied AI math. -------------------------------------------------------------------- 📦 WHAT'S INCLUDED? -------------------------------------------------------------------- * Comprehensive text breaking down the human logic behind each formula. * Production-ready, pure Python code blocks for all 5 functions. * Clear architectural blueprints explaining Forward and Backward propagation. The black box is gone. It’s time to stop consuming AI libraries and start architecting them. 👉 Click "Buy This" to unlock the mathematics of intelligence and claim your engineering superpower today! ====================================================================
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