Building a Machine Learning Toolbox

Building a Machine Learning Toolbox

Scipy is a convenient, easy-to-use python toolkit designed for science and engineering. It includes statistics, optimization, integration, linear algebra modules, Fourier transforms, signal and image processing, ordinary differential equation solvers, and more.

SciPy installation

• When using Pip
  pip install -user scipy
  to install, it is strongly recommended to install the relevant libraries at one time
  pip install --user numpy scipy matplotlib jupyter pandas sympy nose
• use apt-get
  sudo apt-get install python-numpy python-matplotlib ipython ipython-notebook python-pandas python-sympy python-nose
• More refer to the official website
  http://www.scipy.org

Introduction to NLTK

NLTK is an open source project including: python modules, datasets and tutorials for NLP research and development.
NLTK was developed by Steven Bird and Edward Loper in the Department of Computer and Information Sciences at the University of Pennsylvania.
NLTK includes graphical demonstrations and sample data, and it provides tutorials that explain the basic concepts behind the language processing tasks supported by the toolkit.

Installation of NLTK

pip install -U nltk

• For more installation methods, see https://github.com/nltk/nltk/wiki/Installing-Third-Party-Software
  word segmentation usage
  

Scikit-Learn (Introductory library, Naive Bayes, Random Forest, not well implemented in neural networks)

• Scitkit-Learn is a python-based machine learning module based on the BSD open source license. This project was first initiated in 2007 and is currently maintained by community volunteers.
• Related Scikit-Learn resources, module downloads, documentation, routines, etc. can be found on the official website of Scikit-Learn.
• The basic functions of Scikit-Learn are mainly divided into six parts, classification, regression, clustering, data dimensionality reduction, model selection, and data preprocessing. For details, please refer to the documentation on the official website.

Scikit-Learn installation method

• 使用pip pip install -U scikit-learn
• 使用conda conda install scikit-learn
• For more information, please refer to http://scikit-learn.org/

TensorFlow

Tensor Flow data flow

• TensorFlow is an open-source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, and lines in the graph represent multidimensional data arrays, namely tensors, that are interconnected between nodes.
• Its flexible architecture allows you to perform computations on a variety of platforms, such as one or more CPUs (or GPUs) in desktop computers, servers, mobile devices, and more.
• TensorFlow was originally developed by researchers and engineers in the Google Brain Group (part of Google's Machine Intelligence Research Institute) for research in machine learning and deep neural networks, but the system's versatility makes it widely available for other computations as well field.

Features of TensorFlowde

High flexibility, true portability, linking research and products, automatic differentiation, multi-language support, performance optimization

TensorFlowde installation method

• CPU-only version
  pip install https：//storage.googlepis.com/tensorflow/linux/cpu/tensorflow-flow-0.5.0-cp27-none-linux_x86_64.whl
• Open the GPU-supported version (the prerequisite for installing this version is that the CUDA sdk has been installed)
  pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
  Virtualenv-based installation method
  First, install all the necessary tools:
  On LINUX: On sudo apt-get install python-pip python-dev python-virtualenv
  mac:sudo easy_install pip #如果还没有安装pip
sudo pip install --upgrade virtualenv