Glossary
Models
Fundamentals
Adversarial Machine LearningAI AgentsAI and EducationAI and MedicineAI AssistantsAI HardwareAI Voice TransferApproximate Dynamic ProgrammingBackpropagationBayesian Machine LearningCurse of DimensionalityData LabelingDeep LearningDeep Reinforcement LearningDimensionality ReductionF1 Score in Machine LearningFeedforward Neural NetworkFine Tuning in Deep LearningGated Recurrent UnitGenerative AILarge Language Model (LLM)Machine LearningMultimodal LearningNatural Language Generation (NLG)Natural Language Processing (NLP)Natural Language Understanding (NLU)Precision and RecallTransformersZero-shot Classification Models
Models
AlphaGo ZeroBERTChatGPTChess botsDall-EDiffusion ModelsDistilBERTGoogle's BardHidden Markov Models (HMMs)Inference EngineLarge Language Model (LLM)Llama 2LLM CollectionMidjourney (Image Generation)MistralOpenAI WhisperPerceptronProbabilistic Models in Machine LearningRoBERTaRule-Based AISpeech-to-text modelsText-to-Speech ModelsXLNet
Techniques
Activation FunctionsBatch Gradient DescentBeam Search AlgorithmBenchmarkingClassificationCURE AlgorithmExpectation MaximizationFlajolet-Martin AlgorithmGaussian ProcessesGenerative Adversarial Networks (GANs)Gradient Boosting Machines (GBMs)Gradient ClippingGradient ScalingGroundingHyperparametersk-ShinglesLatent Dirichlet Allocation (LDA)Mixture of ExpertsMultimodal AIOnline Gradient DescentOverfitting and UnderfittingPrompt ChainingPrompt EngineeringRetrieval-Augmented Generation (RAG)RLHFSentiment AnalysisSequence ModelingSemantic KernelTokenizationWinnow AlgorithmWord Embeddings
Last updated on February 16, 20241 min read
Models

AI models are computer programs that analyze and find patterns in data to make predictions. Learn more about some of the notable models in the field.

In the heart of every AI system lies a model, acting as the brain, interpreting data, making predictions, and driving intelligent actions.

Foundation and Evolution:

Embark on a historical journey through AI modeling:

  • Early Designs: Explore models that laid the groundwork for AI, from perceptrons to basic neural networks.

  • Learning Paradigms: Understand the diverse learning strategies, be it supervised, unsupervised, or reinforcement learning.

Modern Marvels:

Navigate the contemporary world of AI modeling:

  • Deep Learning: Delve into the depths of convolutional neural networks, recurrent networks, and their multifaceted applications.

  • Transformers & Beyond: Uncover the architecture and power behind revolutionary models like GPT, BERT, and their successors.

Challenges and Frontiers:

Engage with the complexities and the future of AI modeling:

  • Overfitting & Generalization: Tackle the dichotomy of model specificity and adaptability.

  • Interpretable AI: Grasp the push for models that are not just smart but also transparent and explainable.

The "Models" section promises a voyage from the foundational pillars of AI modeling to the cutting-edge structures that are redefining what machines can achieve.

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