{
    "api": "YAML JSON TOON Database",
    "version": "1.0.0",
    "format": "json",
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        "id": 5,
        "slug": "ai-ml-terms",
        "name": "AI & ML Terms",
        "description": "Key terminology in artificial intelligence and machine learning.",
        "icon": "cpu",
        "created_at": 1783090350
    },
    "datasets": [
        {
            "id": 1,
            "slug": "ai-ethics-interpretability",
            "title": "AI Ethics & Interpretability",
            "description": "Key concepts in responsible AI: fairness, bias detection, explainability (XAI), model cards, and regulatory compliance (EU AI Act).",
            "tags": "ai ethics,interpretability,xai,fairness,explainability,regulation,ai-act",
            "view_count": 0,
            "created_at": 1783090350
        },
        {
            "id": 2,
            "slug": "attention-mechanisms",
            "title": "Attention Mechanisms in Deep Learning",
            "description": "Attention architectures: self-attention, multi-head attention, cross-attention, scaled dot-product, sparse attention, and efficient attention variants (Linformer, Performer, FlashAttention).",
            "tags": "attention,self-attention,transformer,multi-head,cross-attention,sparse-attention,flash-attention",
            "view_count": 0,
            "created_at": 1783090350
        },
        {
            "id": 3,
            "slug": "deep-learning-optimizers",
            "title": "Deep Learning Optimizers",
            "description": "Optimization algorithms for training neural networks: SGD, Momentum, NAG, Adam, AdamW, RMSprop, AdaGrad, and learning rate scheduling strategies.",
            "tags": "deep-learning,optimizers,sgd,adam,adamw,rmsprop,learning-rate,momentum",
            "view_count": 0,
            "created_at": 1783090350
        },
        {
            "id": 4,
            "slug": "llm-glossary",
            "title": "Large Language Model Glossary",
            "description": "Key terms and concepts in LLM technology: tokens, context windows, temperature, RAG, fine-tuning, quantization, KV cache, and emergence.",
            "tags": "llm,ai,glossary,nlp",
            "view_count": 0,
            "created_at": 1783090350
        },
        {
            "id": 5,
            "slug": "ml-evaluation-metrics",
            "title": "Machine Learning Evaluation Metrics",
            "description": "Common ML evaluation metrics: Accuracy, Precision, Recall, F1 Score, ROC-AUC, MAE, MSE, and R-squared with formulas and best-use scenarios.",
            "tags": "ml,evaluation,metrics,data science",
            "view_count": 0,
            "created_at": 1783090350
        },
        {
            "id": 6,
            "slug": "ml-model-architectures",
            "title": "Machine Learning Model Architectures",
            "description": "Overview of major ML model families: feedforward neural networks, CNNs, RNNs/LSTMs, Transformers, GANs, and when to use each.",
            "tags": "machine-learning,neural-networks,architectures,cnn,rnn,transformer,gan",
            "view_count": 0,
            "created_at": 1783090350
        },
        {
            "id": 7,
            "slug": "ml-training-algorithms",
            "title": "Machine Learning Training Algorithms",
            "description": "Core ML training algorithms: gradient descent variants, backpropagation, optimization methods (SGD, Adam, RMSprop), and when to use each.",
            "tags": "machine-learning,training,optimization,gradient-descent,backpropagation",
            "view_count": 0,
            "created_at": 1783090350
        },
        {
            "id": 8,
            "slug": "neural-network-types",
            "title": "Neural Network Architectures",
            "description": "Common neural network architectures: CNN, RNN, LSTM, Transformer, GAN, and Diffusion models with their primary use cases and key features.",
            "tags": "neural networks,deep learning,ai,architecture",
            "view_count": 0,
            "created_at": 1783090350
        },
        {
            "id": 9,
            "slug": "regularization-techniques",
            "title": "Regularization Techniques in Machine Learning",
            "description": "Techniques to prevent overfitting: L1/L2 regularization, dropout, batch normalization, early stopping, data augmentation, label smoothing, and mixup.",
            "tags": "regularization,overfitting,dropout,batch-norm,l1,l2,early-stopping,data-augmentation,label-smoothing",
            "view_count": 0,
            "created_at": 1783090350
        },
        {
            "id": 10,
            "slug": "transfer-learning",
            "title": "Transfer Learning and Fine-Tuning",
            "description": "Transfer learning strategies: feature extraction, fine-tuning, domain adaptation, multi-task learning, prompt tuning, LoRA, and pretraining paradigms.",
            "tags": "transfer-learning,fine-tuning,feature-extraction,domain-adaptation,lora,prompt-tuning,pretraining",
            "view_count": 0,
            "created_at": 1783090350
        }
    ]
}