安装deep learning 的 python开发环境

本文详细介绍了在Python环境中搭建DeepLearning教程项目的步骤,包括安装setuptools、scipy、Theano等依赖库的具体方法。

最近在看deep learning的相关知识,从deeplearning.net上下载了一份代码 lisa-lab / DeepLearning

项目主页:https://github.com/lisa-lab/DeepLearningTutorials
代码下载地址:https://github.com/lisa-lab/DeepLearningTutorials/archive/master.zip


需要配置以下环境,具体安装步骤如下:

0、Python最好是2.6以上的,版本低的,记得升级一下。

1、安装setuptools
https://pypi.python.org/pypi/setuptools/ 
python setup.py install 提示错误:Compression requires the (missing) zlib module
yum install zlib
yum install zlib-devel
安装完成后,重新编译 python2.7【不需要删除,只需要重新编译,make,安装就行了】

2、安装scipy
easy_install scipy 
提示错误:Download error on https://pypi.python.org/simple/scipy/: unknown url type: https -- Some packages may not be found!
检查一下你的网络,能否连到互联网上。
如果可以,看一下https协议是否安装,openssl,没有就装一下。
yum install openssl-devel
最好把相关的,都装一下。
yum install openssl
安装完成后,重新编译 python2.7,make,make install

3、安装scipy
http://www.scipy.org/install.html
sudo yum install numpy scipy python-matplotlib ipython python-pandas sympy python-nose

4、安装Theano
easy_install Theano

5、安装PIL
千万记得不要 easy_install PIL,会缺东西
1) 卸载你的PIL (在"/Library/Python/2.7/site-packages/"目录找到PIL,连同PIL.pth一起删除之)
2) yum install libjpeg libjpeg-devel zlib zlib-devel freetype freetype-devel lcms lcms-devel
3) 在这下载PIL: http://effbot.org/downloads/Imaging-1.1.7.tar.gz
4) 解压PIL安装包
    $ tar zxvf Imaging-1.1.7.tar.gz
    $ cd Imaging-1.1.7
5) 编辑解压出来的setup.py, 设置:


6) 安装PIL
   $ python setup.py build_ext -i
   如果和下图的不符合,说明缺少相应的包,去安装!


   $ python selftest.py - Run the selftest to confirm PIL is installed ok
   如果和下图的不符合,说明缺少相应的包,去安装!不然即使执行下一步,后面跑代码的时候还是会挂!


   $ sudo python setup.py install 

转载自:http://blog.youkuaiyun.com/sunlylorn/article/details/18710939

Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems By 作者: Matthew Lamons – Rahul Kumar – Abhishek Nagaraja ISBN-10 书号: 1788997093 ISBN-13 书号: 9781788997096 出版日期: 2018-10-31 pages 页数: (670) Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system. Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects. By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way Contents 1: BUILDING DEEP LEARNING ENVIRONMENTS 2: TRAINING NN FOR PREDICTION USING REGRESSION 3: WORD REPRESENTATION USING WORD2VEC 4: BUILDING AN NLP PIPELINE FOR BUILDING CHATBOTS 5: SEQUENCE-TO-SEQUENCE MODELS FOR BUILDING CHATBOTS 6: GENERATIVE LANGUAGE MODEL FOR CONTENT CREATION 7: BUILDING SPEECH RECOGNITION WITH DEEPSPEECH2 8: HANDWRITTEN DIGITS CLASSIFICATION USING CONVNETS 9: OBJECT DETECTION USING OPENCV AND TENSORFLOW 10: BUILDING FACE RECOGNITION USING FACENET 11: AUTOMATED IMAGE CAPTIONING 12: POSE ESTIMATION ON 3D MODELS USING CONVNETS 13: IMAGE TRANSLATION USING GANS FOR STYLE TRANSFER
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