Java-first AI engineering
Learn to build real AI applications using Spring Boot and Spring AI instead of generic Python-only tutorials.
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Master AI engineering with Java and Spring AI — from prompt engineering and RAG to agents, production resilience, and building neural networks and LLMs from scratch.
Learn to build real AI applications using Spring Boot and Spring AI instead of generic Python-only tutorials.
Go beyond using LLMs — understand and build the math, neural networks, and transformer architecture behind them.
Cover resilience, security, observability, and scalability patterns needed for real enterprise AI systems.
Модули свёрнуты для быстрого просмотра. Откройте любой модуль, чтобы увидеть его темы.
AI, Machine Learning & Generative AI Foundations
What Are LLMs? Models, Providers & Tokens
Context Windows, Temperature & Sampling
Prompt Engineering & Message Roles
Prompt Templates & Structured Output
Spring AI, ChatClient, Memory & Streaming
Spring Boot + Spring AI Application Setup
Structured AI Responses & Conversation Management
Tool Calling and Tool Design
External APIs as Read & Write Tools
Embeddings, Semantic Search & Vector Databases
PostgreSQL + pgvector and Basic RAG
RAG Architecture & Knowledge Ingestion
Website, HTML, PDF & Database Processing
Data Cleaning, Chunking & Chunk Overlap
Metadata, Embedding Pipelines & Vector Search
Hybrid Search, Query Rewriting & Reranking
Citations, Knowledge Synchronization & RAG Evaluation
Model Context Protocol Architecture, Client & Server
MCP Tools, Resources & Enterprise System Integration
What Is an AI Agent? Agent vs Chat vs Workflow
Agent State, Memory, Planning & Reasoning
Agent Loops, Termination & Multi-Agent Communication
Deterministic vs Agentic Workflows & Human-in-the-Loop
AI-Native Architecture, Orchestration & Model Gateway/Router
Event-Driven AI, Kafka & Microservice Integration
Sync vs Async and Multi-Tenant AI Platforms
LLM Failure Modes, Timeouts, Retries & Circuit Breakers
Caching, Semantic Caching & Rate Limiting
Reactive AI, Backpressure & Cost/Token Optimization
AI Threat Model: Prompt Injection, Jailbreaking & RAG Poisoning
Tool Abuse, Authorization, Least Privilege & Data Privacy
Guardrails, Input/Output Validation & Secure Agent Design
AI Evals: Groundedness, Relevance, Correctness & Hallucination Detection
Tool-Calling & Agent Evaluation, Regression Testing
AI Tracing with OpenTelemetry, Micrometer, Prometheus & Grafana
Open-Source & Local LLMs with Ollama
GPU vs CPU Inference and Quantization
Small vs Large Language Models & Reasoning Models
Model Selection, Routing & Multi-Provider Architecture
Cost-, Latency- and Privacy-Based Routing
Linear Algebra: Vectors, Matrices & Cosine Similarity
Probability, Statistics & Distributions
Calculus & Optimization: Gradients and Gradient Descent
Supervised & Unsupervised Learning, Regression & Classification
Overfitting, Regularization & Model Evaluation Metrics
Artificial Neurons, Weights, Bias & Activation Functions
Forward Propagation and Loss Functions
Backpropagation and the Chain Rule in Neural Networks
Weight Initialization and Optimization
Building a Neural Network from Scratch
Training with PyTorch and GPU Training
Tokenization, Vocabulary, Token IDs & Embeddings
Self-Attention: Query, Key, Value & Attention Scores
Multi-Head Attention and Transformer Blocks
Decoder-Only Transformers and Next-Token Prediction
Building a Tokenizer, Attention & a Small GPT-Style Model
Training the Model and Generating Text
Pretraining, Instruction Tuning & Post-Training
Supervised Fine-Tuning, LoRA & Quantization
Fine-Tuning Open Models, Serving & Deployment
Computer Vision: CNNs, Image Classification & Vision Transformers
Multimodal AI: Vision-Language Models and Multimodal RAG
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