machine-learning - Concepts

Explore concepts tagged with "machine-learning"

Total concepts: 38

Concepts

Instruction Tuning - A fine-tuning technique that trains language models to follow natural language instructions by learning from examples of instruction-response pairs.
Reinforcement Learning from Human Feedback (RLHF) - A training technique that aligns LLM outputs with human preferences by using human feedback to guide model behavior.
Semantic Search - A search technique that finds information based on meaning and intent rather than exact keyword matching.
Model Collapse - The degradation of AI model quality when trained on synthetic data generated by other AI models, causing progressive loss of diversity and accuracy.
Reward Model - A neural network trained to predict human preferences, used to provide a scalar reward signal for optimizing language model behavior in RLHF.
Deep Learning - A subset of machine learning using neural networks with multiple layers to learn complex patterns from data.
Edge AI - Running artificial intelligence models directly on local devices (phones, IoT sensors, cars) rather than in the cloud, enabling faster responses and greater privacy.
Knowledge Distillation - A model compression technique where a smaller student model is trained to reproduce the behavior and outputs of a larger, more capable teacher model.
Diffusion Models - Generative AI models that learn to create data by progressively denoising random noise into coherent outputs.
Speculative Decoding - An inference acceleration technique where a smaller draft model proposes multiple tokens that a larger target model verifies in parallel, speeding up generation without changing output quality.
Mixture of Experts - A neural network architecture that uses a gating network to route inputs to specialized sub-networks called experts, enabling efficient scaling by activating only a subset of parameters for each input.
Sparse Models - Neural network architectures where only a fraction of parameters are activated for any given input, enabling larger model capacity with lower computational cost.
Goldilocks Rule for AI - The principle that AI tasks should be neither too easy nor too hard to maintain engagement and optimal learning.
Reward Hacking - A failure mode in reinforcement learning where an agent exploits flaws in the reward function to achieve high reward without fulfilling the intended objective.
Algorithmic Bias - Systematic errors in AI and automated systems that create unfair outcomes, often reflecting or amplifying human biases present in training data or design choices.
Direct Preference Optimization - A simplified alternative to RLHF that fine-tunes language models directly on human preference data without training a separate reward model.
Unsupervised Learning - A machine learning approach where models find patterns in data without labeled examples or predefined outcomes.
Big Data - Datasets so large, fast-moving, or complex that traditional data processing methods cannot handle them effectively, characterized by volume, velocity, variety, veracity, and value.
Vector Store - A specialized database designed to store, index, and search high-dimensional vector embeddings for AI applications.
Reinforcement Learning - A machine learning paradigm where an agent learns to make decisions by taking actions in an environment and receiving rewards or penalties as feedback.
Model Quantization - A technique for reducing the numerical precision of a neural network's weights and activations to decrease model size, memory usage, and inference latency.
Ensemble Learning - A machine learning paradigm that combines predictions from multiple models to produce more accurate and robust results than any single model alone.
Connectionism - Connectionism is a cognitive science approach that models mental processes using artificial neural networks of simple interconnected units processing information in parallel through weighted connections.
Natural Language Processing - The field of artificial intelligence focused on enabling computers to understand, interpret, and generate human language.
Machine Learning - A subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed.
Neural Networks - Computing systems inspired by biological neural networks in the brain, designed to recognize patterns and learn from data.
Model Pruning - A neural network compression technique that removes redundant or low-impact weights, neurons, or entire layers to create smaller, faster models.
Zero-Shot Learning - AI performing tasks based on instructions alone, without any specific examples.
Multi-Task Learning - A machine learning approach where a single model is trained on multiple related tasks simultaneously, leveraging shared representations to improve generalization.
Model Scaling - The study and practice of increasing neural network size, data, or compute to improve model performance, guided by empirical scaling laws.
Gating Network - A neural network component that learns to route inputs to the most appropriate expert sub-networks in mixture of experts architectures.
Supervised Learning - A machine learning approach where models are trained on labeled data with known correct outputs.
Few-Shot Learning - Training or prompting AI with just a few examples to perform new tasks.
AI Inference - The process of running a trained machine learning model to generate predictions, classifications, or outputs from new input data.
Federated Learning - A distributed machine learning approach where models are trained across multiple decentralized devices or servers holding local data, without exchanging raw data.
RAG Pipelines - Data processing workflows that handle the end-to-end flow from document ingestion to LLM response generation in Retrieval-Augmented Generation systems.
Fine-Tuning - Customizing pre-trained AI models by training them further on specific data or tasks.
Large Language Models (LLMs) - AI models that use transformer architecture to understand and generate human-like text by predicting the next token in a sequence.

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