api: "YAML JSON TOON Database"
version: 1.0.0
format: toon
category:
  id: 67
  slug: ai-ml-terms
  name: "AI & ML Terms"
  description: "Key terminology in artificial intelligence and machine learning."
  icon: cpu
  created_at: 1781275418
datasets
  [10]{id,slug,title,description,tags,view_count,created_at}:
    360,ai-ethics-interpretability,"AI Ethics & Interpretability","Key concepts in responsible AI: fairness, bias detection, explainability (XAI), model cards, and regulatory compliance (EU AI Act).","ai ethics,interpretability,xai,fairness,explainability,regulation,ai-act",0,1781275786
    363,attention-mechanisms,"Attention Mechanisms in Deep Learning","Attention architectures: self-attention, multi-head attention, cross-attention, scaled dot-product, sparse attention, and efficient attention variants (Linformer, Performer, FlashAttention).","attention,self-attention,transformer,multi-head,cross-attention,sparse-attention,flash-attention",3,1781275786
    396,deep-learning-optimizers,"Deep Learning Optimizers","Optimization algorithms for training neural networks: SGD, Momentum, NAG, Adam, AdamW, RMSprop, AdaGrad, and learning rate scheduling strategies.","deep-learning,optimizers,sgd,adam,adamw,rmsprop,learning-rate,momentum",0,1781275786
    440,llm-glossary,"Large Language Model Glossary","Key terms and concepts in LLM technology: tokens, context windows, temperature, RAG, fine-tuning, quantization, KV cache, and emergence.","llm,ai,glossary,nlp",3,1781275786
    451,ml-evaluation-metrics,"Machine Learning Evaluation Metrics","Common ML evaluation metrics: Accuracy, Precision, Recall, F1 Score, ROC-AUC, MAE, MSE, and R-squared with formulas and best-use scenarios.","ml,evaluation,metrics,data science",3,1781275786
    452,ml-model-architectures,"Machine Learning Model Architectures","Overview of major ML model families: feedforward neural networks, CNNs, RNNs/LSTMs, Transformers, GANs, and when to use each.","machine-learning,neural-networks,architectures,cnn,rnn,transformer,gan",1,1781275786
    453,ml-training-algorithms,"Machine Learning Training Algorithms","Core ML training algorithms: gradient descent variants, backpropagation, optimization methods (SGD, Adam, RMSprop), and when to use each.","machine-learning,training,optimization,gradient-descent,backpropagation",3,1781275786
    461,neural-network-types,"Neural Network Architectures","Common neural network architectures: CNN, RNN, LSTM, Transformer, GAN, and Diffusion models with their primary use cases and key features.","neural networks,deep learning,ai,architecture",3,1781275786
    478,regularization-techniques,"Regularization Techniques in Machine Learning","Techniques to prevent overfitting: L1/L2 regularization, dropout, batch normalization, early stopping, data augmentation, label smoothing, and mixup.","regularization,overfitting,dropout,batch-norm,l1,l2,early-stopping,data-augmentation,label-smoothing",3,1781275786
    501,transfer-learning,"Transfer Learning and Fine-Tuning","Transfer learning strategies: feature extraction, fine-tuning, domain adaptation, multi-task learning, prompt tuning, LoRA, and pretraining paradigms.","transfer-learning,fine-tuning,feature-extraction,domain-adaptation,lora,prompt-tuning,pretraining",0,1781275786