Evaluating and Improving Deep Neural Networks

深度神经网络最近推动了图像理解、语言理解、基因组学、计算化学、游戏和机器人等领域的最新应用。特别是深度生成模型在过去几年中取得了快速进展，网络能够产生可信的图像或语音信号。尽管这些应用领域各不相同，但研究人员和实践者面临的挑战和挫折有很多共同点。在昂贵的图形处理单元（GPU）硬件上训练神经网络可能需要数周时间。与上一代机器学习算法相比，该算法有更多需要调整的旋钮。在深度生成模型的情况下，很难确定网络是在学习对数据建模，还是只是记忆它们的训练样本。

Deep neural networks have recently pushed forward the state-of-the-art in applications as diverse as image understanding, language understanding, genomics, computational chemistry, game playing, and robotics. Deep generative models in particular have seen rapid progress in the past few years, with networks able to produce plausible images or speech signals. As diverse as these application areas are, the challenges and frustrations facing the researchers and practitioners have much in common. Neural networks can take weeks to train on expensive Graphics Processing Unit (GPU) hardware. The algorithms have many more knobs which need to be tweaked, compared with the previous generation of machine learning algorithms. In the case of deep generative models, it can be hard to determine if the networks are learning to model the data or simply memorizing their training examples.