ModelRefs / AI & ML Tutorials
AI & ML Tutorials
Step-by-step tutorials to learn AI, machine learning and modern LLM engineering.
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AI & ML Tutorials: This hub organizes related ModelRefs references into a crawlable starting point. Use it to narrow the problem, identify relevant profiles or guides, and continue into detailed evidence and implementation material.
AI & ML Tutorials: Items are connected across models, providers, benchmarks, workflows, tools, and guides. Those relationships explain where an option fits, what it depends on, and which adjacent decisions still need validation.
AI & ML Tutorials: Catalogue presence is not an endorsement or universal ranking. Compare candidates against your own requirements and review each page's sources, freshness notes, limitations, and coverage gaps.
All tutorials (92)
Every published tutorial.
- Activation Functions (ReLU, Sigmoid, Softmax)
- AI Observability (Prometheus, Grafana)
- AI vs ML vs Deep Learning
- Anomaly Detection
- Attention Mechanism
- Backpropagation
- Bayes' Theorem
- BERT & Encoder Models
- Bias-Variance Tradeoff
- Build a Production RAG Stack
- Build a Real-Time Voice AI
- Building Your First Network
- Calculus & Derivatives
- Classification Basics
- Convolutional Neural Networks
- Create Representative Workload Evaluations
- Cross-Validation
- Data Preprocessing
- Data Visualization
- Decision Trees
- Docker & Containerization for AI
- Eigenvalues & Eigenvectors
- Embeddings & Semantic Search
- Feature Engineering
- Few-Shot & Zero-Shot Learning
- Fine-Tuning LLMs
- Function Calling & Tool Use
- GPT & Decoder Models
- Gradient Descent
- Guardrails & Safety Controls
- Hierarchical Clustering
- History of AI
- How LLMs Are Trained
- How to Build a Custom MCP Server in Python (Tools, Resources, Prompts)
- How to Build an MCP Agent That Bridges Claude and a Local Ollama Model
- How to Deploy a Remote MCP Server (Streamable HTTP + OAuth 2.1)
- How to Evaluate Prompts Programmatically (Build a Reusable Eval Harness)
- How to Test an AI Agent Safely (Evaluation, Sandboxing, Red-Teaming)
- How to Use LLM-as-a-Judge (Reliable Scoring for Open-Ended Outputs)
- Hugging Face Ecosystem
- Introduction to AI
- Introduction to Neural Networks
- K-Means Clustering
- K-Nearest Neighbors
- Kubernetes for ML
- LangChain Framework
- Linear Algebra Essentials
- Linear Regression
- LlamaIndex
- Logistic Regression
- LoRA & PEFT
- LSTMs & GRUs
- MCP Security: How to Defend Against Tool Poisoning and Prompt Injection
- Measure Retrieval Recall and Grounding
- Memory Systems
- MLflow Experiment Tracking
- Model Deployment with FastAPI
- Model Evaluation Metrics
- Model Quantization & ONNX
- Multi-Agent Networks
- Multimodal Document Intelligence Pipeline
- NumPy Fundamentals
- Optimization: SGD, Momentum, and Adam
- Pandas Data Analysis
- Perceptrons & Feedforward Networks
- Planning & Reasoning Loops
- Principal Component Analysis
- Probability & Statistics for ML
- Prompt Engineering
- Python Basics for AI
- PyTorch Basics
- Random Forests
- Recurrent Neural Networks
- Regularization
- Reinforcement Learning Intro
- Self-Attention & Multi-Head Attention
- Ship an Agentic System
- SQL & Databases for AI
- Supervised Learning
- Support Vector Machines
- TensorFlow & Keras
- The Transformer Architecture
- Tokenization
- Transfer Learning
- Types of AI (Narrow, General, Super)
- Unsupervised Learning
- Vector Databases
- Vectors & Matrices
- What are AI Agents?
- What are Large Language Models?
- What is Machine Learning?
- What is RAG?
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