The Popular Deep Learning Libraries for Data Science

Nov. 2, 2017, 7:57 p.m. By: Kirti Bakshi

Deep Learning

The Combination of an abundance of data with the advancement in computer science and statistics has given rise to a new professional ecosystem called Data Science-That at its very core remains about the generation of insight from data in order to inform decision making. Coming to Open-Source Deep Learning Libraries there are many in the List that Prove to be very useful in Data-Science, The image Below shows the ranking of the TOP 23 Libraries that have been ranked on equally after weighing its three components using available API's: Github(Stars and Forks), Google Results(Quarterly and Total growth rate) and finally stack Overflow(Questions and Tags).

Deep Learning Libraries

Here, We'll Discuss and get a basic Introduction on the Top-5 Open Source Deep Learning Libraries in the list as per the Ranking in the Above Image:

1. TensorFlow:

An Open Source software library that is used for Data-flow programming for a number of tasks and for Machine Learning applications such as Neural Networks is today dominating the field with the largest active community.

The key feature is that the library uses a system of multi-layered nodes that allow you to set up and quickly train artificial neural networks with large datasets.

TensorFlow, released under the Apache 2.0 open source license in the year 2015 was developed by the Google Brain team for internal Google use and derives its name from operations performed on multidimensional data arrays such as by Neural Networks- referred to as "tensors".

2. Keras:

Keras, whose primary author and maintainer is a Google engineer, François Chollet was developed as part of the research effort of ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System). It is the Highest Ranked non-framework Library.

Keras is an open-source library, that is minimalistic, modular, extendable, pure Python based and is used for building Neural Networks at a high-level of the interface. Its approach in design aims at easy and fast experimentation through the building of compact systems. The general idea is based on layers and Data is prepared in tensors where the first layer is responsible for input of tensors, the last layer for output, and the model is built in between.

3. Caffe:

Caffe is a deep learning framework that takes the third strongest place in the above list and supports many different types of deep learning architectures that are geared towards image segmentation and image classification. This open source, under a BSD license library, was originally developed at UC Berkeley with its stable release in 2017 and is written with a Python interface in C++ with its original author being- Yangging Jia.

It is currently being used in startup prototypes, academic research projects, speech, multimedia and even more.

4. Theano:

Theano is an open source project that was primarily developed at the Université de Montréal by a machine learning group and continues to be the oldest library in the rankings to hold a top spot without any large industry Support.

This library uses syntax like NumPy to optimize and evaluate mathematical expressions and is a numerical computation library for Python, that can also be used with high-level deep-learning wrappers. What makes it different is that it takes advantage of the computer’s GPU to make data-intensive calculations up to 100 times faster than the CPU.

5. PyTorch:

PyTorch, a GPU ready Tensor Library, is a deep learning framework that is the second fastest growing library and whose sole interface is in Python and puts it first. It provides two main features:

  • Tensor computation with strong GPU acceleration.

  • Deep Neural Networks built on a tape-based autograd system.

So, These were a few libraries that are on the top 5 of the ranking and are worth looking at and as well as familiarizing yourself with!

Source: GitHub