Glossary

Dec 19, 2025

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8 min read

Few-Shot Learning

Few-Shot Learning enables models to learn new tasks from minimal examples through meta-learning and transfer learning approaches, dramatically reducing labeled data requirements compared to traditional supervised learning.

Glossary

Dec 18, 2025

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7 min read

Hallucination

Hallucinations occurs when language models generate factually incorrect or fabricated content that appears plausible, stemming from knowledge gaps, architectural constraints, and the challenge of generating coherent text without true understanding.

Glossary

Dec 17, 2025

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8 min read

Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation combines external information retrieval with language model generation, grounding responses in retrieved documents to reduce hallucinations and provide factually accurate, attributable answers.

Glossary

Dec 16, 2025

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8 min read

Fine-tuning

Fine-tuning adapts pre-trained models to specific tasks by continuing training on smaller, domain-specific datasets, dramatically reducing computational requirements while preserving pre-trained knowledge and capabilities.

Glossary

Dec 15, 2025

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8 min read

Prompt Engineering

Prompt Engineering designs and optimizes text inputs to guide language models toward desired outputs, enabling task adaptation through careful instruction formulation without requiring model retraining or fine-tuning.

Glossary

Dec 12, 2025

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8 min read

Embeddings

Embeddings represent data as dense vectors in continuous spaces where geometric relationships encode semantic similarity, enabling neural networks to perform mathematical operations on symbolic information.

Glossary

Dec 12, 2025

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8 min read

Tokens

Tokens represent discrete units created through tokenization that break text into manageable pieces for language model processing, balancing vocabulary size, sequence length, and computational efficiency.

Glossary

Dec 12, 2025

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9 min read

Large Language Models (LLMs)

Large Language Models employ transformer architectures with billions of parameters trained on massive text corpora, enabling sophisticated language understanding, reasoning, and generation through self-supervised learning at scale.

Glossary

Dec 11, 2025

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7 min read

Natural Language Processing

Natural Language Processing combines computational linguistics with machine learning to enable computers to understand, interpret, and generate human language in written and spoken forms.

Glossary

Dec 11, 2025

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9 min read

Loss Function

Loss functions quantify prediction error, providing differentiable objectives that guide neural network optimization through gradient descent toward minimized model performance metrics.

Glossary

Dec 11, 2025

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7 min read

Gradient Descent

Gradient descent iteratively adjusts model parameters by moving in the direction of steepest descent to minimize loss functions, forming the foundational optimization approach for training neural networks.

Glossary

Dec 10, 2025

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5 min read

Multi-Head Attention

Multi-head attention runs multiple independent attention operations in parallel, enabling models to simultaneously capture different types of relationships and patterns from different representation subspaces.

Glossary

Dec 10, 2025

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5 min read

Self-Attention

Self-attention enables each position in a sequence to attend to all other positions, capturing relationships and dependencies regardless of distance while allowing full parallel processing.

Glossary

Dec 10, 2025

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5 min read

Attention Mechanism

Attention mechanisms enable neural networks to dynamically assign importance weights to input elements, allowing models to focus on relevant information rather than processing all inputs equally.

Glossary

Dec 9, 2025

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4 min read

Transformer Architecture

Transformers have revolutionized deep learning by enabling parallel processing of sequences through attention mechanisms, becoming the foundation for modern large language models like GPT and BERT.

Glossary

Dec 9, 2025

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4 min read

Long Short-Term Memory

Recurrent neural networks process sequential data by maintaining memory states that evolve across time steps, enabling networks to leverage temporal context and long-range dependencies.

Glossary

Dec 9, 2025

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3 min read

Recurrent Neural Networks

Recurrent neural networks process sequential data by maintaining memory states that evolve across time steps, enabling networks to leverage temporal context and long-range dependencies.

Glossary

Dec 8, 2025

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4 min read

Convolutional Neural Networks

Convolutional neural networks apply learned filters across images to detect spatial hierarchies of features, from simple edges to complex objects, revolutionizing computer vision tasks.

Glossary

Dec 8, 2025

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4 min read

Backpropagation

Backpropagation connects error measurements to parameter adjustments by computing error gradients, enabling networks to learn patterns from data iteratively.

Glossary

Dec 8, 2025

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3 min read

Activation Function

Activation functions introduce non-linearity into neural networks, enabling them to learn complex relationships that would be impossible with linear transformations alone.

Glossary

Dec 8, 2025

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4 min read

Neural Networks

Neural networks consist of interconnected artificial neurons organized in layers that automatically discover relevant features and patterns in data without explicit programming.

Glossary

Dec 8, 2025

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4 min read

Hyperparameters

Hyperparameters are configuration settings that control the learning process and must be set before training, fundamentally shaping model performance and training efficiency.

Glossary

Dec 8, 2025

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4 min read

Generalization

Generalization enables machine learning models to transfer learned knowledge to real-world scenarios by maintaining performance on data significantly different from training examples.

Glossary

Dec 8, 2025

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4 min read

Overfitting

Overfitting represents the challenge of balancing model complexity against generalization, where models that perform perfectly on training data fail to predict accurately on new instances.

Glossary

Dec 8, 2025

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5 min read

Cross-validation

Cross-validation partitions data into multiple subsets to provide robust, reliable estimates of model performance and generalization to new, unseen data.

Glossary

Few-Shot Learning

Hallucination

Retrieval-Augmented Generation (RAG)

Fine-tuning

Prompt Engineering

Embeddings

Tokens

Large Language Models (LLMs)

Natural Language Processing

Loss Function

Gradient Descent

Multi-Head Attention

Self-Attention

Attention Mechanism

Transformer Architecture

Long Short-Term Memory

Recurrent Neural Networks

Convolutional Neural Networks

Backpropagation

Activation Function

Neural Networks

Hyperparameters

Generalization

Overfitting

Cross-validation

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