#ml

मशीन-सहायता अनुवाद मसौदा (Hindi) 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.

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

मशीन-सहायता अनुवाद मसौदा (Hindi) 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.

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

मशीन-सहायता अनुवाद मसौदा (Hindi) 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.

“उदाहरण मसौदा: Inference latency dropped from 2 seconds to 200ms after switching to a quantized model.”

मशीन-सहायता अनुवाद मसौदा (Hindi) 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.

“उदाहरण मसौदा: The neural network learned to recognize handwritten digits with over 99% accuracy.”

मशीन-सहायता अनुवाद मसौदा (Hindi) 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.

“उदाहरण मसौदा: We generated synthetic medical records to train the model without risking patient privacy.”