Python Deep Learning 简明教程

Python Deep Learning - Introduction

深度结构学习或分层学习，简称深度学习，是机器学习方法的一个分支部分本身是更广泛的人工智能领域的子集。

Deep structured learning or hierarchical learning or deep learning in short is part of the family of machine learning methods which are themselves a subset of the broader field of Artificial Intelligence.

深度学习是一类机器学习算法，它使用多层非线性处理单元进行特征提取和转换。每一后续层使用前一层的输出作为输入。

Deep learning is a class of machine learning algorithms that use several layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input.

深度神经网络、深度信念网络和递归神经网络已应用于计算机视觉、语音识别、自然语言处理、音频识别、社交网络过滤、机器翻译和生物信息学等领域，在这些领域中它们产生的结果可与专家相比，在某些情况下甚至优于专家。

Deep neural networks, deep belief networks and recurrent neural networks have been applied to fields such as computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, and bioinformatics where they produced results comparable to and in some cases better than human experts have.

深度学习算法和网络−

Deep Learning Algorithms and Networks −

  1. are based on the unsupervised learning of multiple levels of features or representations of the data. Higher-level features are derived from lower level features to form a hierarchical representation.

  2. use some form of gradient descent for training.