Neonode 的数据培训方法可提供准确、自适应的数据模型，能够快速应对现实世界的变化。
数据是任何机器学习应用的核心。要让机器学习模型学会识别模式、对象或行为，就需要在大量数据上进行训练，这些数据代表了模型应该学习的内容。
For computer vision tasks, training data consists of images together with ground truth annotations. If you’re teaching a model to find faces, these annotations could tell the model if there is a face in the training image and where in the image the face is located. When AI behaves unexpectedly, the root cause is often found in the training data used for teaching the machine learning model. For example, if the ‘face dataset’ doesn’t include any pictures of faces in the profile, the final model will have a hard time recognizing human heads.
在本白皮书中，Agnes Jernström 解释了 Neonode 如何使用合成数据来训练我们的神经网络，以确保尽可能高的准确性。