一、深度学习与机器学习的区别
1、特征提取方面
机器学习：人工特征提取 + 分类算法
深度学习：没有人工特征提取，直接将特征值传进去
（1）机器学习的特征工程步骤是要靠手工完成的，而且需要大量领域专业知识
（2）深度学习通常由多个层组成，它们通常将更简单的模型组合在一起，将数据从一层传递到另一层来构建更复杂的模型。通过训练大量数据自动得出模型，不需要人工特征提取环节
（3）深度学习算法试图从数据中学习高级功能，这是深度学习的一个非常独特的部分。因此，减少了为每个问题开发新特征提取器的任务。适合用在难提取特征的图像、语音、自然语言处理领域
2、数据量和计算性能要求
机器学习需要的执行时间远少于深度学习，深度学习参数往往很庞大，需要通过大量数据的多次优化来训练参数
（1）深度学习需要大量的训练数据集
（2）训练深度神经网络需要大量的算力
（3）可能需要数天、甚至数周的时间，才能使用数百万张图像的数据集训练出一个深度网络
所以深度学习通常：
需要强大的GPU服务器来进行计算
全面管理的分布式训练与预测服务
3、算法代表
（1）机器学习
朴素贝叶斯、决策树等
（2）深度学习
神经网络
二、深度学习的应用场景
1、图像识别
（1）物体识别
（2）场景识别
（3）车型识别
（4）人脸检测跟踪
（5）人脸关键点定位
（6）人脸身份认证
2、自然语言处理技术
（1）机器翻译
（2）文本识别
（3）聊天对话
3、语音技术
（1）语音识别
三、深度学习框架介绍
1、常见深度学习框架对比
这是一张2015-2016年的图表，2015年11月谷歌将TensorFlow开源，那时候国内开始卷java好像[笑哭][笑哭][笑哭]
说明：
（1）最常用的框架当属TensorFlow和Pytorch，而Caffe和Caffe2次之
（2）PyTorch和Torch更适用于学术研究（research）；TensorFlow、Caffe、Caffe2更适用于工业界的生产环境部署（industrial production）
（3）Caffe适用于处理静态图像（static graph）；Torch和PyTorch更适用于动态图像（dynamic graph）；TensorFlow在两种情况下都很实用
（4）TensorFlow和Caffe2可在移动端使用
2、TensorFlow的特点
官网：https://tensorflow.google.cn/?hl=zh-cn
（1）高度灵活
它不仅可以用来做神经网络算法研究，也可以用来做普通的机器学习算法，甚至是只要把计算表示成数据流图，都可以用TensorFlow
（2）语言多样性
TensorFlow使用C++实现，然后用Python封装
（3）设备支持
TensorFlow可以运行在各种硬件上，同时根据计算的需要，合理将运算分配到相应的设备，比如卷积就分配到GPU上，也允许在CPU和GPU上的计算分布
（4）Tensorboard可视化
因为深度学习训练出来的模型，参数非常非常多，网络层数也非常非常的多，可视化可以帮助你展示
3、TensorFlow的安装
（1）CPU版本
pip install -U tensorflow
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注：GPU版本适用于带有CUDA核心的NV显卡，英特尔的核显，AMD的显卡不行
（3）CPU版本和GPU版本对比
CPU：核心的数量更少，但是每一个核心的速度更快，性能更强，更适用于处理连续性（sequential）任务
GPU：核心的数量更多，但是每一个核心的处理速度较慢，更适合于并行（parallel）任务
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