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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.

61 topics•3 categories•Updated November 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

43 entries

Generative, agentic and emerging AI, models and frameworks.

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. 18Causal AI ModelsCausal inference, causal discovery, libraries such as DoWhy and EconML, and learning resources. 19Graph & Causal Representation LearningNode embeddings, graph neural networks, graph transformers, causal representation learning, causal discovery, libraries and benchmarks. 20AI AssistantsChat assistants and alternatives. 21AI Agent FrameworksFrameworks for building AI agents. 22Quantum AIQuantum AI hub. 23Neuromorphic AIBrain-inspired computing. 24Physical AIRobot foundation models, world models, simulation and the machines that embody AI. 25Swarm IntelligencePSO, ant colony and bee algorithms, optimization libraries and swarm robotics. 26Probability Theory for AIBayesian inference, probabilistic and generative models, uncertainty and conformal prediction, and bandits. 27Linear Algebra for AIVectors, matrices and tensors, SVD and PCA, attention and embeddings, and low-rank methods such as LoRA. 28Generative AI ArchitecturesTransformers, diffusion models, GANs, VAEs and state space models, and how they compare. 29Small Language Models (SLMs)Compact models for on-device and low-cost use, such as Gemma, Qwen, Phi and SmolLM. 30Vision Language Models (VLMs)Models that read images and text together, with current open and proprietary options. 31Multimodal Language ModelsModels that handle text, images, audio and video, from GPT and Gemini to open omni models. 32LLM FrameworksLangChain, LlamaIndex, DSPy, vLLM and other frameworks for building, tuning, serving and evaluating LLM apps. 33Vector DatabasesPinecone, Weaviate, Milvus, Qdrant, pgvector and other stores for embeddings and similarity search. 34Large Action Models (LAMs)Computer-use and GUI agents and models that turn instructions into actions. 35Enterprise AI Security ArchitectureTwelve layers for securing enterprise AI, from data and models to governance and resilience. 36LLM HubIndex of guides and directories on language models, from training and inference to RAG and LLMOps. 37AI Governance FrameworksRegulation, standards, roles and controls for AI. 38Explainable AI (XAI) FrameworksSHAP, LIME, InterpretML and other tools for explaining model behavior. 39AI Coding AgentsCursor, Claude Code, Codex, Copilot and other coding agents. 40AI Chip ProvidersGoogle Cloud TPU, Arm, Apple, Intel and other AI chip providers. 41Artificial General Intelligence (AGI)Definitions, benchmarks and safety for AGI. 42Knowledge Graphs & Graph TechnologiesKnowledge graphs, graph databases and semantic graph technologies. 43RoboticsRobotics resources, research and products, including robot action models. 44Data SciencePlatforms, notebooks, data wrangling and models. 45AI Courses and CertificationsMachine learning, generative AI and cloud AI courses and certifications. 46Neuro-Symbolic AICombining neural learning with symbolic reasoning. 47Prompt EngineeringWriting effective prompts for LLMs and agents. 48Context EngineeringDesigning the context that LLM applications and agents work from. 49Large Reasoning ModelsTest-time reasoning models that plan, verify and think before answering. 50Tabular Foundation ModelsTabPFN, TabICL and TabFM for tabular data. 51Diffusion ModelsDDPM, Stable Diffusion and flow models. 52Agent MemoryShort- and long-term memory layers for AI agents. 53LLMOpsOperating LLM applications in production. 54AI Model Optimization TechniquesPruning, quantization and distillation. 55AI Agent Evaluation ToolsFrameworks and tools for evaluating agents. 56Reinforcement Learning from Human Feedback (RLHF)RLHF papers, PPO, DPO, RLAIF, libraries, preference datasets and reward model benchmarks.

Related Pages

5 entries

Neighbouring directories.

57Edge AIAI at the edge. 58AI ArchitectureData, models, RAG, agents and gateways, on this site. 59Cloud AIAI platforms, inference, GPUs and MLOps delivered from the cloud. 60MLOps PlatformsPipelines, registries and monitoring for ML in production. 61AI GatewaysRouting, securing and metering model calls.

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.