Python Deep Learning 简明教程

Python Deep Learning - Applications

深度学习已经为一些应用产生了良好的结果，例如计算机视觉、语言翻译、图像字幕、音频转录、分子生物学、语音识别、自然语言处理、自动驾驶汽车、脑肿瘤检测、实时语音翻译、音乐创作、自动游戏游戏等等。

Deep learning has produced good results for a few applications such as computer vision, language translation, image captioning, audio transcription, molecular biology, speech recognition, natural language processing, self-driving cars, brain tumour detection, real-time speech translation, music composition, automatic game playing and so on.

深度学习是机器学习之后的下一次重大飞跃，采用了更先进的实现方式。目前，它正朝着成为行业标准的方向发展，有望在处理原始非结构化数据时成为游戏规则改变者。

Deep learning is the next big leap after machine learning with a more advanced implementation. Currently, it is heading towards becoming an industry standard bringing a strong promise of being a game changer when dealing with raw unstructured data.

深度学习目前是各种现实世界问题最佳的解决方案提供者之一。开发人员正在构建人工智能程序，这些程序不再使用先前给定的规则，而是从示例中学习解决复杂的任务。随着许多数据科学家使用深度学习，更深层次的神经网络正在提供越来越准确的结果。

Deep learning is currently one of the best solution providers fora wide range of real-world problems. Developers are building AI programs that, instead of using previously given rules, learn from examples to solve complicated tasks. With deep learning being used by many data scientists, deeper neural networks are delivering results that are ever more accurate.

其理念是通过增加每个网络的训练层数来开发深度神经网络；机器更多地了解数据，直至尽可能准确。开发人员可以使用深度学习技术来实现复杂机器学习任务，并训练人工智能网络以获得高水平的感知识别。

The idea is to develop deep neural networks by increasing the number of training layers for each network; machine learns more about the data until it is as accurate as possible. Developers can use deep learning techniques to implement complex machine learning tasks, and train AI networks to have high levels of perceptual recognition.

深度学习在计算机视觉中很受欢迎。此处实现的任务之一是图像分类，其中给定的输入图像被分类为猫、狗等，或作为最能描述图像的类别或标签。作为人类，我们在生命早期就学会了如何执行此任务，并且具备快速识别模式、从先验知识中概括和适应不同图像环境的技能。

Deep learning finds its popularity in Computer vision. Here one of the tasks achieved is image classification where given input images are classified as cat, dog, etc. or as a class or label that best describe the image. We as humans learn how to do this task very early in our lives and have these skills of quickly recognizing patterns, generalizing from prior knowledge, and adapting to different image environments.