Training data is the set of examples a machine learning model studies in order to learn. The “Machine Learning Basics” chapter of Goodfellow, Bengio, and Courville’s “Deep Learning” treats the training set as the experience from which a learning algorithm improves, and stresses that the goal is to perform well on new data drawn from the same distribution, not just to fit the training examples.
The scale and quality of training data often matter as much as the algorithm. The ImageNet dataset, introduced in Deng, Dong, Socher, Li, Li, and Fei-Fei’s 2009 CVPR paper “ImageNet: A Large-Scale Hierarchical Image Database,” assembled millions of labeled images and became the benchmark that helped trigger the deep learning revolution in computer vision. It showed that large, well-organized data could unlock dramatic gains.
Because models learn whatever patterns are present in their data, biased, incomplete, or mislabeled training data produces biased or unreliable models.
Why business readers should care: Training data is usually the single biggest factor in whether an AI project succeeds, and often the most expensive to get right. Garbage in, garbage out applies directly: investing in clean, representative, well-labeled data typically pays off more than chasing a fancier algorithm.