Pylearn2 vs. Scikit-learn: Python Libraries

With advancements in technology and the need to hone it in the most innovative ways, machine learning and deep learning are becoming more and more popular with each passing second. It is gaining popularity to an extent where several organizations and academia have started taking assistance from python development services in New Jersey to develop efficient tools and libraries. For instance, let us choose tech giants like Microsoft, Google, and Facebook heavily investing in building dynamic and robust deep learning models. And when we talk about deep learning projects, we instantly think about Python's most popular programming language. Developers world wideconsider this language one of the most suitable languages due to a plethora of libraries and tools available for performing machine learning tasks.

There is no denying that Python is an ocean of libraries that serve a plethora of purposes in web development and other operations. There are a plethora of reasons why Python is popular among developers. One of them is that it has an amazingly extensive collection of libraries that users can work with. The simplicity of Python has attracted many developers to create new libraries for machine learning.  

In this article, we will compare the two popular Python machine learning libraries, Pylearn 2, and sci-kit learn. Before delving into the intricate details about these libraries, let us get you familiar with the basics and all.

Scikit-Learn 

Scikit-learn is a popular machine learning library in Python language built on top of SciPy, NumPy, and Matplotlib. It is a library that supports supervised and supervised learning and provides various tools for data processing, model fitting, model selection, and evaluation, among many other utilities. The primary functions of scikit-learn are divided into regression, clustering, classification, model selection, dimensionality reduction, and data reprocessing. 

There are a plethora of changes being made in this library. The cross-validation feature is one of those significant changes that provide the ability to use more than one metric. Lots of training methods like nearest neighbors and logistics regression have received some little improvements.  

Features of Scikit-learn 

• This Python library is useful for extracting features from images and text. 

• This library offers various methods to check the accuracy of supervised models on unseen data. 

• There is a broad spread of algorithms in the library- starting from factor analysis, principal component analysis, clustering to unsupervised neural networks.

• It is open-source in nature. 

• It is accessible to everybody and reusable in various contexts. 

• It is efficient and straightforward for productive data analysis.

These features are what make the developers take the assistance of Python Software Development in Philadelphia.

Pylearn 2

Pylearn 2 is a popular machine learning research library developed by LISA. Most of the functionality in this library is built on top of Theano. This library aims to facilitate machine learning research by focusing on flexibility and extensibility. This library also makes sure that any research idea is feasible to implement in the library or not. Pylearn2 achieves extensibility and flexibility by decomposing into reusable parts. A few components are used to fulfill most features that include dataset, model, and training Algorithm classes.

Features of Pylearn 2

• This Python library offers easy experimentation into a plethora of processes and operations.  

• Pylearn aims to provide high flexibility and make it possible for a researcher to do almost anything.

• This library supports cross-platform serialization of learned models. 

• The library provides a domain-specific language that provides a compact way of specifying all hyperparameters for an experiment. 

• It provides easy reuse of sub-component. 

Comparing Sci-kit learn and Pylearn 2

Both Scikit-learn and Pylearn 2 are ideal for operations like data analysis and data mining in machine learning. The most common differences between these two popular libraries are as follows: 

As we have already mentioned that Pylearn 2 aims to provide high flexibility and make it easy and convenient for a researcher to do almost anything. Scikit-learn aims to work as a black box that can produce excellent results even if the user fails to understand the implementation. 

While using Pylearn 2, the user must be an expert practitioner who must understand how the algorithm works to accomplish basic data analysis tasks. And if we talk about the scikit-learn library, you might not necessarily understand how the underlying algorithm works.

Wrapping up 

The current scenario has led the Pylearn project to be discontinued being maintained as no developers are bringing about the updates. But if you, as an entrepreneur or businessman, consult experts for python development services, the developers would continue to review, pull requests, and merge them when appropriate. To lay on a verdict, we would say, Scikit-learn is currently being maintained by the community members and an excellent option to be preferred over Pylearn-2.