Deep learning with limited data

深度神经网络是一种基于类神经元模型的机器学习技术。这里的“深度”指的是具有能够表示复杂功能的许多层的网络。这种模型已经被证明是非常有价值的，因为它们可以从示例中学习分层数据表示法，并预测以前未见过的数据。这些技术使计算机视觉和模式识别领域最近取得的许多进展成为可能，而深度神经网络的进步则是由于大数据集(Big Data)的可用性、算法的一些改进以及使用图形处理单元(GPU)等专用处理器提高计算能力的可用性。谷歌、微软、亚马逊和Facebook等公司，以及许多规模较小的工程公司和市场分析公司都在拥抱这项新技术。

Deep neural networks is a machine learning technique based on a model of neuron-like elements. The "deep" refers here to networks with many layers that are capable of representing complex functions. Such models have been proven to be extremely valuable as they can learn hierarchical data representations from examples and predict previously unseen data. Such techniques enabled much of the recent progress in computer vision and pattern recognition, and the advancements of deep neural networks was made possible by the availability of large data sets (Big Data), some refinement of algorithms, and the availability of increased computational power with specialized processors such as graphical processing units (GPUs). Companies like Google, Microsoft, Amazon and Facebook, and also numerous smaller engineering firms and market analysis firms are embracing this new technology.