Introduction to Deep Learning Libraries
In this article, we will see what are the different Deep learning libraries available in the market and how those libraries are effective in solving the challenges in the field of deep learning. As we all know that deep learning is a subset in the field of machine learning, and the deep learning algorithms mostly work on the unstructured data, and at the heart of the deep learning is the deep neural networks. Deep neural networks are basically nothing but an arrangement of the neurons in such a way that the operations performed by those neurons and the network appear similar to that of the human brain.
Let’s see what are the deep learning libraries which are currently available in the market, so what basically is meant by the library.
The library is a set of methods and classes written in a particular programming language that can be used directly to solve the problem on the go. The libraries are generally written by highly professional software developers and are tried and tested before going public with them. The open-source libraries are generally available for free and anyone can use it and modify it as per his/her liking.
Different Libraries of Deep Learning
All the libraries which are generally used for deep learning are open source and few of them are as follows:
- TensorFlow
- deeplearning4j
- Torch
- Caffe
- Microsoft CNTK
- ML.NET
- Theano
- Deepmat
- Neon
In this article, we will discuss TensorFlow, Theano, deeplearning4j, Torch, and Caffe. Since these libraries are the most popular and widely used libraries in the field of deep learning. For commercial use, TensorFlow, deeplearning4j, torch, and Caffe are used and for research and education purposes Theano is used.
1. TensorFlow
- TensorFlow is the machine learning and deep learning library developed by Google and it came into the market around the 2016 march.
- TensorFlow grew out of an in-house library of google brain known as DistBelief.
- Currently, TensorFlow is the leading and most used library in the market.
- Different types of deep nets can be developed and also the various packages available in this library are used to attain and address most of the tasks and problems in the field of deep learning.
- This library is completely written in python and so it’s easy to use for the python programmers.
- Due to a flexible computational graphical structure of TensorFlow, this library is not only limited to deep learning operations it can be used for many different operations and applications.
- TensorFlow provides a layer or we can say more of a wrapper, known as Keras which is used to access the different packages and methods easily of TensorFlow.
- Google provides a very well documentation for this library where every small intricacies and usage are mentioned anyone can refer that and use the library.
- TensorFlow is a very fast-evolving library, this library can be used for educational purposes as well as huge large scale commercial application can also be built.
- Google has developed this library for Mobil platforms as well which is known as TensorFlow lite.
- TensorFlow is the only library which provides support for Python, Java, C++, javascript and swift programming language, for Javascript TensorFlow.js
- TensorFlow has also support for GPU and big data.
2. Deeplearning4j
- Deeplearning4j is the open-source java library which only supports java programming language and this library is written in Java.
- This was developed by Adam Gibson to provide distributed multimode capabilities for deep neural networks.
- This library is very much use full for the application which is having build on top of big data.
- This library works with Scala and also provide inbuilt GPU support.
3. Torch
- This open-source deep-learning library was developed by Facebook and Twitter.
- This library is written in Lua programming language.
- However PyTorch is the library which is widely used, and it’s written in a python programming language
4. Caffe
- Caffe is an open-source deep-learning library written in C++/CUDA and developed by Yangqing Jia of Google.
- This library was first developed for computer vision tax.
- Caffe gives permission to the user to configure the hyperparameters for a deep net.
- The layer configuration is very robust and very much sophisticated.
5. Theano
- This is the open-source deep-learning library written in Python and CUDA.
- This library is very similar to the TensorFlow library but the implementation and usage are not that simple as that of TensorFlow.
- This library is generally used for educational and research purposes.
- Theano is not that easy to use and many deep learning libraries extend the features of this library to help ease the life of the developer for coding the deep learning models.
- Theano is fastest amongst most of the libraries mentioned because it makes use of vectors and matrices for all the functions and the vectorized code runs faster since parallel processing for the multiple values makes the things faster.
6. Microsoft CNTK
- This is a cognitive toolkit developed by Microsoft to venture in the field of Artificial intelligence.
- This library is written in python and its supports the other packages and libraries which python programming language supports, and it comes with Microsoft visual studio.
- CNTK is used to describe neural networks as a series of computational directed graphs.
7. ML.NET
- ML.NET is the open-source library which is also developed by Microsoft for the dot net developers.
- This library is written in C# and F# and it uses the Microsoft dot net platform.
- With the library, it becomes easy to create desktop as well as large scale web applications which can bring the vast possibility of the machine learning algorithm to the end-user.
8. Deepmat
- This library is developed in MATLAB.
- With the use of this library, we can implement deep learning using MATLAB
- with this library GSN, CNN, Restricted Boltzmann machine, Deep belief networks,multi-layer perceptron, and many more artificial neural networks.
9. Neon
- Neon is a deep learning framework created by the Nervana systems to deliver industry-leading cutting edge technologies.
- This framework has been depreciated as of 2018 and further research has been carried out by Intel corporation on the same.
- As per the Intel corporation website, alternative frameworks are asked to be used such as
Intel optimization for tensorFlow, Intel optimization for Caffe, pytorch, etc.
Conclusion
In this article, we have seen what exactly is deep learning and what are all the different libraries present to implement deep learning. We have also seen an overview of all the deep learning libraries used in the market and what are its advantages and applications of all those libraries.
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