EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login
Home Data Science Data Science Tutorials Machine Learning Tutorial Deep Learning Libraries
Secondary Sidebar
Machine Learning Tutorial
  • Deep Learning
    • What Is Deep learning
    • Overviews Deep Learning
    • Application of Deep Learning
    • Careers in Deep Learnings
    • Deep Learning Frameworks
    • Deep Learning Model
    • Deep Learning Algorithms
    • Deep Learning Technique
    • Deep Learning Networks
    • Deep Learning Libraries
    • Deep Learning Toolbox
    • Types of Neural Networks
    • Convolutional Neural Networks
    • Create Decision Tree
    • Deep Learning for NLP
    • Caffe Deep Learning
    • Deep Learning with TensorFlow
  • Basic
    • Introduction To Machine Learning
    • What is Machine Learning?
    • Uses of Machine Learning
    • Applications of Machine Learning
    • Naive Bayes in Machine Learning
    • Dataset Labelling
    • DataSet Example
    • Deep Learning Techniques
    • Dataset ZFS
    • Careers in Machine Learning
    • What is Machine Cycle?
    • Machine Learning Feature
    • Machine Learning Programming Languages
    • What is Kernel in Machine Learning
    • Machine Learning Tools
    • Machine Learning Models
    • Machine Learning Platform
    • Machine Learning Libraries
    • Machine Learning Life Cycle
    • Machine Learning System
    • Machine Learning Datasets
    • Machine Learning Certifications
    • Machine Learning Python vs R
    • Optimization for Machine Learning
    • Types of Machine Learning
    • Machine Learning Methods
    • Machine Learning Software
    • Machine Learning Techniques
    • Machine Learning Feature Selection
    • Ensemble Methods in Machine Learning
    • Support Vector Machine in Machine Learning
    • Decision Making Techniques
    • Restricted Boltzmann Machine
    • Regularization Machine Learning
    • What is Regression?
    • What is Linear Regression?
    • Dataset for Linear Regression
    • Decision tree limitations
    • What is Decision Tree?
    • What is Random Forest
  • Algorithms
    • Machine Learning Algorithms
    • Apriori Algorithm in Machine Learning
    • Types of Machine Learning Algorithms
    • Bayes Theorem
    • AdaBoost Algorithm
    • Classification Algorithms
    • Clustering Algorithm
    • Gradient Boosting Algorithm
    • Mean Shift Algorithm
    • Hierarchical Clustering Algorithm
    • Hierarchical Clustering Agglomerative
    • What is a Greedy Algorithm?
    • What is Genetic Algorithm?
    • Random Forest Algorithm
    • Nearest Neighbors Algorithm
    • Weak Law of Large Numbers
    • Ray Tracing Algorithm
    • SVM Algorithm
    • Naive Bayes Algorithm
    • Neural Network Algorithms
    • Boosting Algorithm
    • XGBoost Algorithm
    • Pattern Searching
    • Loss Functions in Machine Learning
    • Decision Tree in Machine Learning
    • Hyperparameter Machine Learning
    • Unsupervised Machine Learning
    • K- Means Clustering Algorithm
    • KNN Algorithm
    • Monty Hall Problem
  • Supervised
    • What is Supervised Learning
    • Supervised Machine Learning
    • Supervised Machine Learning Algorithms
    • Perceptron Learning Algorithm
    • Simple Linear Regression
    • Polynomial Regression
    • Multivariate Regression
    • Regression in Machine Learning
    • Hierarchical Clustering Analysis
    • Linear Regression Analysis
    • Support Vector Regression
    • Multiple Linear Regression
    • Linear Algebra in Machine Learning
    • Statistics for Machine Learning
    • What is Regression Analysis?
    • Clustering Methods
    • Backward Elimination
    • Ensemble Techniques
    • Bagging and Boosting
    • Linear Regression Modeling
    • What is Reinforcement Learning
  • Classification
    • Kernel Methods in Machine Learning
    • Clustering in Machine Learning
    • Machine Learning Architecture
    • Automation Anywhere Architecture
    • Machine Learning C++ Library
    • Machine Learning Frameworks
    • Data Preprocessing in Machine Learning
    • Data Science Machine Learning
    • Classification of Neural Network
    • Neural Network Machine Learning
    • What is Convolutional Neural Network?
    • Single Layer Neural Network
    • Kernel Methods
    • Forward and Backward Chaining
    • Forward Chaining
    • Backward Chaining
  • RPA
    • What is RPA
    • What is Robotics?
    • Benefits of RPA
    • RPA Applications
    • Types of Robots
    • RPA Tools
    • Line Follower Robot
    • What is Blue Prism?
    • RPA vs BPM
  • Interview Questions
    • Deep Learning Interview Questions And Answer
    • Machine Learning Cheat Sheet

Related Courses

Machine Learning Training

Deep Learning Training

Artificial Intelligence Training

Deep Learning Libraries

By Priya PedamkarPriya Pedamkar

deep-learning-liabriries

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 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 deep learning libraries 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 a few of them are as follows:

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

  • 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.

All in One Data Science Bundle(360+ Courses, 50+ projects)
Python TutorialMachine LearningAWSArtificial Intelligence
TableauR ProgrammingPowerBIDeep Learning
Price
View Courses
360+ Online Courses | 50+ projects | 1500+ Hours | Verifiable Certificates | Lifetime Access
4.7 (86,650 ratings)

1. TensorFlow

  • TensorFlow is the machine learning and deep learning library developed by Google and it came into the market around 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 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 very well documentation for this library where every small intricacies and usage are mentioned anyone can refer to 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 applications 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 that 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 provides 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 that 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 the 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 things faster.

6. Microsoft CNTK

  • This is a cognitive toolkit developed by Microsoft to venture into the field of Artificial intelligence.
  • This library is written in python and it 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 deprecated 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 the advantages and applications of all those libraries.

Recommended Articles

This is a guide to Deep Learning Libraries. Here we discuss the most popular and widely used libraries in the field of deep learning. You may also look at the following articles to learn more –

  1. Deep Learning Frameworks
  2. What is Machine Learning Platform?
  3. Application of Deep Learning
  4. What Is Deep learning
Popular Course in this category
All in One Data Science Bundle (360+ Courses, 50+ projects)
  360+ Online Courses |  1500+ Hours |  Verifiable Certificates |  Lifetime Access
4.7
Price

View Course

Related Courses

Machine Learning Training (20 Courses, 29+ Projects)4.9
Deep Learning Training (18 Courses, 24+ Projects)4.8
Artificial Intelligence AI Training (5 Courses, 2 Project)4.7
0 Shares
Share
Tweet
Share
Primary Sidebar
Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Live Classes
  • Corporate Training
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Database Management
  • Machine Learning
  • All Tutorials
Certification Courses
  • All Courses
  • Data Science Course - All in One Bundle
  • Machine Learning Course
  • Hadoop Certification Training
  • Cloud Computing Training Course
  • R Programming Course
  • AWS Training Course
  • SAS Training Course

ISO 10004:2018 & ISO 9001:2015 Certified

© 2022 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA
Free Data Science Course

SPSS, Data visualization with Python, Matplotlib Library, Seaborn Package

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA Login

Forgot Password?

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA
Free Design Course

3D animation, modelling, simulation, game development & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

Let’s Get Started

By signing up, you agree to our Terms of Use and Privacy Policy.

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

Loading . . .
Quiz
Question:

Answer:

Quiz Result
Total QuestionsCorrect AnswersWrong AnswersPercentage

Explore 1000+ varieties of Mock tests View more