门户网站建设必要性,免费行情软件下载,360平台怎么做网站优化,如何把网站放到空间别人可以访问一、深度学习与机器学习的区别
1、特征提取方面 机器学习#xff1a;人工特征提取 分类算法 深度学习#xff1a;没有人工特征提取#xff0c;直接将特征值传进去
#xff08;1#xff09;机器学习的特征工程步骤是要靠手工完成的#xff0c;而且需要大量领域专业知识…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 2PyTorch和Torch更适用于学术研究researchTensorFlow、Caffe、Caffe2更适用于工业界的生产环境部署industrial production 3Caffe适用于处理静态图像static graphTorch和PyTorch更适用于动态图像dynamic graphTensorFlow在两种情况下都很实用 4TensorFlow和Caffe2可在移动端使用
2、TensorFlow的特点 官网https://tensorflow.google.cn/?hlzh-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 4Tensorboard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
1CPU版本
pip install -U tensorflowCollecting tensorflowDownloading tensorflow-2.6.2-cp36-cp36m-manylinux2010_x86_64.whl (458.3 MB)|████████████████████████████████| 458.3 MB 16 kB/s
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Building wheels for collected packages: clang, termcolor, wraptBuilding wheel for clang (setup.py) ... doneCreated wheel for clang: filenameclang-5.0-py3-none-any.whl size30694 sha2564b478abb7303e2ab6ceae5dd321630fe487cdbb7229b1c9109dbbda97b6f6de0Stored in directory: /root/.cache/pip/wheels/22/4c/94/0583f60c9c5b6024ed64f290cb2d43b06bb4f75577dc3c93a7Building wheel for termcolor (setup.py) ... doneCreated wheel for termcolor: filenametermcolor-1.1.0-py3-none-any.whl size4848 sha25685b28ee5cc23acde89b4124855db5fec4d3b5bc9f09d22853e2a5d32f869232fStored in directory: /root/.cache/pip/wheels/93/2a/eb/e58dbcbc963549ee4f065ff80a59f274cc7210b6eab962acdcBuilding wheel for wrapt (setup.py) ... doneCreated wheel for wrapt: filenamewrapt-1.12.1-cp36-cp36m-linux_x86_64.whl size64570 sha2565313bb733d9d37abf00e4ce2533656facec5e4518e11aaafb7dbb3171cd1bcaaStored in directory: /root/.cache/pip/wheels/32/42/7f/23cae9ff6ef66798d00dc5d659088e57dbba01566f6c60db63
Successfully built clang termcolor wrapt
Installing collected packages: urllib3, pyasn1, idna, charset-normalizer, certifi, typing-extensions, six, rsa, requests, pyasn1-modules, oauthlib, cachetools, requests-oauthlib, google-auth, werkzeug, tensorboard-plugin-wit, tensorboard-data-server, protobuf, markdown, grpcio, google-auth-oauthlib, cached-property, absl-py, wrapt, termcolor, tensorflow-estimator, tensorboard, opt-einsum, keras-preprocessing, keras, h5py, google-pasta, gast, flatbuffers, clang, astunparse, tensorflowAttempting uninstall: typing-extensionsFound existing installation: typing-extensions 4.1.1Uninstalling typing-extensions-4.1.1:Successfully uninstalled typing-extensions-4.1.1Attempting uninstall: sixFound existing installation: six 1.16.0Uninstalling six-1.16.0:Successfully uninstalled six-1.16.0
Successfully installed absl-py-0.15.0 astunparse-1.6.3 cached-property-1.5.2 cachetools-4.2.4 certifi-2024.2.2 charset-normalizer-2.0.12 clang-5.0 flatbuffers-1.12 gast-0.4.0 google-auth-1.35.0 google-auth-oauthlib-0.4.6 google-pasta-0.2.0 grpcio-1.48.2 h5py-3.1.0 idna-3.6 keras-2.6.0 keras-preprocessing-1.1.2 markdown-3.3.7 oauthlib-3.2.2 opt-einsum-3.3.0 protobuf-3.19.6 pyasn1-0.5.1 pyasn1-modules-0.3.0 requests-2.27.1 requests-oauthlib-1.3.1 rsa-4.9 six-1.15.0 tensorboard-2.6.0 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tensorflow-2.6.2 tensorflow-estimator-2.6.0 termcolor-1.1.0 typing-extensions-3.7.4.3 urllib3-1.26.18 werkzeug-2.0.3 wrapt-1.12.1
2GPU版本 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
3CPU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
