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Knowledge Hub

Artificial Intelligence: Algorithms, Enterprise AI, Vision, NLP, Platforms & Tools

The entry point into AI — how systems learn on their own, where AI creates value in an enterprise, how machines see and how they understand language, plus the platforms and tools around them.

34 topics•4 categories•Updated October 2026

Six Ways In

7 entries

The six core AI hubs on this site.

01AI Algorithms: Self-Learning AlgorithmsThe learning mechanisms underneath AI applications. 02Enterprise AIWhere AI paradigms meet enterprise deployment, risk and infrastructure. 03Computer VisionResources, research, platforms and pose estimation models. 04Natural Language ProcessingPlatforms, annotation tools, vendors and language models. 05AI PlatformsCurated AI platform categories, from AIOps to data annotation. 06AI ToolsCurated AI tool categories, from coding tools to cognitive computing. 07Open Source AI ToolsOpen source frameworks, libraries and runtimes: PyTorch, Transformers, vLLM, llama.cpp, Ultralytics and more.

Learning Paradigms

6 entries

Core machine learning branches.

08Machine LearningMachine learning hub. 09Machine Learning ModelsLogistic regression, random forests, CatBoost and tabular foundation models such as TabPFN-3.5. 10Deep LearningDeep learning frameworks, datasets and GNNs. 11Predictive AIPredictive AI and forecasting. 12Reinforcement LearningReinforcement learning hub. 13Reinforcement Learning AlgorithmsValue-based, policy-based and actor-critic algorithms, plus RLHF, GRPO, offline RL and libraries.

Go Deeper

18 entries

Generative, agentic and emerging AI.

14Generative AIGenerative AI hub. 15Agentic AIAgentic AI hub. 16Large Language ModelsLarge language models and assistants. 17LLM Inference EnginesvLLM, SGLang, TensorRT-LLM, llama.cpp and other engines that serve and run models. 18AI Model RoutersRouters that send each prompt or coding task to the best-fit LLM for cost, quality and speed, from OpenRouter to Databricks Smart Routing. 19LLM-as-a-Judge SolutionsEvaluation platforms, frameworks, judge models and bias research for scoring LLM outputs. 20Causal AI ModelsCausal inference, causal discovery, libraries such as DoWhy and EconML, and learning resources. 21Graph & Causal Representation LearningNode embeddings, graph neural networks, graph transformers, causal representation learning, causal discovery, libraries and benchmarks. 22AI AssistantsChat assistants and alternatives. 23AI Agent FrameworksFrameworks for building AI agents. 24Top Agentic AI Frameworks14 agentic AI frameworks with GitHub stars, from LangGraph and CrewAI to AutoGen and LlamaIndex, plus guidance on choosing one. 25Quantum AIQuantum AI hub. 26Neuromorphic AIBrain-inspired computing. 27Physical AIRobot foundation models, world models, simulation and the machines that embody AI. 28Swarm IntelligencePSO, ant colony and bee algorithms, optimization libraries and swarm robotics. 29Number Theory for AIModular arithmetic, cryptography for private AI, grokking, and AI for discovering and proving mathematics. 30Probability Theory for AIBayesian inference, probabilistic and generative models, uncertainty and conformal prediction, and bandits. 31Linear Algebra for AIVectors, matrices and tensors, SVD and PCA, attention and embeddings, and low-rank methods such as LoRA.

Related Pages

3 entries

Neighbouring directories.

32HyperautomationHyperautomation hub. 33Data ScienceData science hub. 34Edge AIAI at the edge.

Frequently Asked Questions

What is artificial intelligence?

AI is the field of building systems that perform tasks normally requiring human intelligence, such as learning, perception, language understanding and decision-making.

What are the main branches of AI?

Machine learning and deep learning, computer vision, natural language processing, reinforcement learning, and newer areas such as generative and agentic AI.

What is enterprise AI?

Applying AI inside organisations, with attention to data, infrastructure, governance and risk as well as models.

How do AI platforms and tools differ?

Platforms provide end-to-end environments for building and running AI; tools address single jobs such as coding, annotation or monitoring.

Where should I start?

Begin with the Machine Learning hub for fundamentals, then follow Computer Vision, NLP or Generative AI depending on your goal.