AI & ML Terms reference data

Datasets in AI & ML Terms

• AI Ethics & Interpretability

  Key concepts in responsible AI: fairness, bias detection, explainability (XAI), model cards, and regulatory compliance (EU AI Act).

• 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).

• Deep Learning Optimizers

  Optimization algorithms for training neural networks: SGD, Momentum, NAG, Adam, AdamW, RMSprop, AdaGrad, and learning rate scheduling strategies.

• Large Language Model Glossary

  Key terms and concepts in LLM technology: tokens, context windows, temperature, RAG, fine-tuning, quantization, KV cache, and emergence.

• 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.

• 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 Training Algorithms

  Core ML training algorithms: gradient descent variants, backpropagation, optimization methods (SGD, Adam, RMSprop), and when to use each.

• Neural Network Architectures

  Common neural network architectures: CNN, RNN, LSTM, Transformer, GAN, and Diffusion models with their primary use cases and key features.

• Regularization Techniques in Machine Learning

  Techniques to prevent overfitting: L1/L2 regularization, dropout, batch normalization, early stopping, data augmentation, label smoothing, and mixup.

• Transfer Learning and Fine-Tuning

  Transfer learning strategies: feature extraction, fine-tuning, domain adaptation, multi-task learning, prompt tuning, LoRA, and pretraining paradigms.

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