Перейти до вмісту

Головна Основи Linux Хмара AI AI/ML Сертифікації Платформа On-Premises

Обрати тему

Обрати мову

Головна Основи Linux Хмара AI AI/ML Сертифікації Платформа On-Premises

AI/ML EngineeringReference

AI/ML Engineering

Prerequisites

Prerequisites Prerequisites & Environment Setup Home AI Workstation Fundamentals Reproducible Python, CUDA, and ROCm Environments Notebooks, Scripts, and Project Layouts

Build AI Apps

AI-Native Development

AI-Native Development AI Coding Tools Landscape Local Models for AI Coding Configuring an Agentic Harness Agent-First IDEs CLI AI Coding Agents AI-Powered Code Generation AI-Assisted Debugging & Optimization Building with AI Coding Assistants Anthropic Agent SDK and Runtime Patterns

Generative AI

Generative AI Introduction to Large Language Models Tokenization & Text Processing Text Generation & Sampling Strategies Embeddings & Semantic Search Vector Space Visualization Reasoning Models: System 2 Thinking

Vector Search & RAG

Vector Search & RAG Vector Databases Deep Dive Building RAG Systems Advanced RAG Patterns RAG Evaluation & Optimization Long-Context LLMs and Prompt Caching Home-Scale RAG Systems

Frameworks & Agents

Frameworks & Agents LangChain Fundamentals LangChain Advanced LangGraph for Agents LlamaIndex Building AI Agents Agent Memory & Planning Multi-Agent Systems Model Context Protocol (MCP) for Agents Computer Use and Browser Automation Agents Next-Gen Agentic Frameworks

Synthesis Apps

Synthesis Apps LLM-Native Stack: Inference and Memory on Kubernetes Wiring the LLM App: The Orchestration Layer Production Gates: LLM Evals in CI Agent Observability with OpenTelemetry

Model Foundations

Deep Learning Foundations

From-Scratch Foundations (NumPy)

1.1 · NumPy, Pandas & Data Tooling 1.1.1 · Neural Network Math Warm-Up 1.1.2 · The Neuron & Perceptron from Scratch 1.1.3 · Forward Propagation in MLPs 1.1.4 · Activation Functions in Depth 1.1.5 · Loss Functions & Output Heads 1.1.6 · Backprop by Hand for Dense Nets 1.1.7 · Tiny NumPy NN Lab 1.1.8 · Computational Graphs & Scalar Autograd

PyTorch & the Training Loop

1.2 · The PyTorch Bridge 1.3 · The Training Loop 1.3.1 · Initialization & Signal Propagation 1.3.2 · Optimizers & LR Dynamics 1.3.3 · Regularization & Generalization 1.3.4 · Normalization Layers 1.3.5 · Training-Diagnostics Playbook 1.3.6 · Numerical Stability & Precision

Representations, Attention & Transformers

1.4.1 · Embeddings & Representation Learning 1.4.2 · Residual Connections & Deep Architectures 1.4.3 · Attention from Scratch 1.4.4 · The Transformer Block from Scratch

Architectures & Capstone

1.5 · CNNs & Computer Vision 1.6 · Recurrent Networks & Sequence Models 1.7 · Capstone: Train a Real Net End-to-End

Advanced Topics

1.8 · Self-Supervised Learning 1.9 · Graph Neural Networks 1.10 · Modern Transformers: RoPE & Attention Variants

Machine Learning

Machine Learning Scikit-learn API & Pipelines Linear & Logistic Regression with Regularization Model Evaluation, Validation, Leakage & Calibration Feature Engineering & Preprocessing Decision Trees & Random Forests XGBoost & Gradient Boosting Naive Bayes, k-NN & SVMs Unsupervised Learning: Clustering Anomaly Detection & Novelty Detection Dimensionality Reduction Hyperparameter Optimization Time Series Forecasting Class Imbalance & Cost-Sensitive Learning ML Interpretability + Failure Slicing Probabilistic & Bayesian ML with PyMC Recommender Systems Conformal Prediction & Uncertainty Quantification Fairness & Bias Auditing Causal Inference for ML Practitioners

Reinforcement Learning

Reinforcement Learning RL Practitioner Foundations Offline RL & Imitation Learning

Operate & Scale

MLOps & LLMOps

MLOps & LLMOps ML DevOps Foundations Docker for ML CI/CD for ML Kubernetes for ML Advanced Kubernetes Experiment Tracking Data Pipelines ML Pipelines Model Serving ML Monitoring Notebooks to Production for ML/LLMs Small-Team Private AI Platform

AI Infrastructure

AI Infrastructure Cloud AI Services AIOps High-Performance LLM Inference: vLLM and sglang Local Inference Stack for Learners Home AI Operations and Cost Model GPU Memory Hierarchy and Bandwidth Math for LLM Inference Production-Tier LLM Inference Engines: Decision Framework Benchmarking LLM Inference: TTFT, TPOT, and Workload-Aware Load Shaping

Advanced GenAI & Safety

Advanced GenAI & Safety Fine-tuning LLMs LoRA & Parameter-Efficient Fine-tuning Diffusion Models RLHF & Alignment Advanced Generation Techniques LLM Evaluation AI Red Teaming AI Safety & Alignment Modern PEFT: DoRA and PiSSA Single-GPU Local Fine-Tuning Multi-GPU and Home-Lab Fine-Tuning Reasoning-Model RL: GRPO, RLVR, and DeepSeek-R1

Multimodal AI

Multimodal AI Voice & Audio AI Vision AI Video AI Multimodal-First AI Design

Reference

Bridges to Other Tracks

AI/ML Engineering Bridges From AI Builder to AI Platform Engineer From Home Lab AI to Private AI Infrastructure

Appendix A: History of AI/ML

Appendix A: History of AI/ML History of AI & Machine Learning

Обрати тему

Обрати мову

На цій сторінці

Огляд
Modules

На цій сторінці

Огляд
Modules

Appendix A: History of AI/ML

Цей контент ще не доступний вашою мовою.

AI/ML Engineering Track | Appendix

Modules

Section titled “Modules”

#	Module
1.1	History of AI & Machine Learning

KubeDojo — Безкоштовна, всеосяжна хмарна освіта. GitHub · Статус · Журнал змін · Мій прогрес