#ml

Brouillon de traduction automatique (French) for "Fine-Tuning": The process of further training a pre-trained model on a smaller, task-specific dataset to adapt its behavior for a particular domain or style. Fine-tuning updates the model's weights to make it perform better on specific tasks without training from scratch.

“Exemple en brouillon: We fine-tuned the base model on our legal contracts corpus so it could draft clauses in the right style.”

Brouillon de traduction automatique (French) for "Embeddings": Dense numerical vector representations of words, sentences, or other data that capture semantic meaning. Similar concepts have similar embeddings (nearby in vector space), allowing AI systems to measure meaning similarity mathematically rather than relying on exact keyword matches.

“Exemple en brouillon: The search engine uses embeddings to find relevant results even when the query words don't appear in the document.”

Brouillon de traduction automatique (French) for "Inference": The act of running a trained machine learning model on new input data to generate predictions or outputs. Inference is distinct from training — it is the "serving" phase where the model is used in production, and its speed and cost are critical for real-world applications.

“Exemple en brouillon: Inference latency dropped from 2 seconds to 200ms after switching to a quantized model.”

Brouillon de traduction automatique (French) for "Neural Network": A computational model loosely inspired by biological neurons, consisting of interconnected layers of mathematical functions (nodes) that transform input data into output predictions. Neural networks learn by adjusting the weights of connections through exposure to training data.

“Exemple en brouillon: The neural network learned to recognize handwritten digits with over 99% accuracy.”

Brouillon de traduction automatique (French) for "Synthetic Data": Artificially generated data that mimics the statistical properties of real-world data, used for training or testing AI models. Synthetic data can be created by generative models, rule-based systems, or simulations, and is especially valuable when real data is scarce, sensitive, or expensive to collect.

“Exemple en brouillon: We generated synthetic medical records to train the model without risking patient privacy.”