Python Machine Learning Library recommendations

python is an object-oriented, interpretive computer programming language with a rich and powerful library, coupled with its simplicity, ease of learning, speed, open source free, portability, extensibility, and object-oriented features,python Become the most popular programming language of the 2017!

AI is one of the most popular topics, machine learning technology is an artificial intelligence to achieve the necessary skills, Python programming language contains the most useful machine learning tools and libraries, the following is the Python development engineers must know the top ten machine learning library!

First,Scikit-learn

In machine learning and data mining applications, Scikit-learn is a powerful Python package that we can use for classification, feature selection, feature extraction, and aggregation.

Second,Statsmodels

Statsmodels is another powerful library focused on statistical models, primarily for predictive and exploratory analysis, fitting linear models, statistical analysis, or predictive modeling, and using Statsmodels is a good fit.

Third,PYMC

PYMC is a tool for "Bayesian curve", which contains Bayesian model, statistical distribution and model convergence diagnostic tools, but also contains some hierarchical models.

Iv.Gensim

Gensim, known as the "People's Theme Modeling Tool", focuses on Dirichlet partitioning and variants, which support natural language processing, make it easier to combine NLP and other machine learning algorithms, and reference Google's recursive neural network-based text notation Word2vec.

Five,Orange

Orange is a library with a graphical user interface that is quite complete in terms of classification, aggregation, and feature selection methods, as well as cross-validation methods.

Liu,pymvpa

PYMVPA is a statistical learning library that includes cross-validation and diagnostic tools, but not scikit-learn comprehensive.

Seven,Theano

Theano is the most mature deep learning library that provides a nice data structure representing the layers of a neural network, is efficient for linear algebra, resembles a numpy array, and many Theano-based libraries are using their data structures, which also support out-of-the-box GPU programming.

Eight,Pylearn

Pylearn is a Theano-based library that introduces modularity and configuration to Theano, which can be used to create neural networks through different configuration files.

Nine,Hebel

Hebel is a neural network library with GPU support that determines the properties of the neural network through YAML files, provides a way to separate the Divine Network from code-friendly, and runs the model quickly, written in pure Python and is a friendly library, but because of the depth and the vast majority of developments, There are some shortages!

Ten,Neurolab

Neurolab is an API-friendly neural network library that contains different variants of the recursive neural network implementation, and if you use RNN, this library is one of the best choices for the same kind of API.

The above is the Python Development Engineer must know ten machine learning Library, in addition, there are overfeat, Nolearn and decaf, such as machine learning Library, in this will not be introduced, interested in in-depth study!

Python Machine Learning Library recommendations