With Artificial Intelligence and Machine Learning becoming an integral part of every enterprise’s digital journey, the choice of a deep learning framework is critical for mission success.
Unlike traditional analytical programs, deep learning frameworks are made up of neural networks. They need massive amounts of labeled data, high-power computing environments and complicated data models with countless parameters.
To make things easier, there are several deep learning frameworks available today that help in data extraction, classification, and processing for achieving business objectives.
Each deep learning framework has a different offering in place which makes it a perfect fit for one scenario while a wrong choice for others. In today’s post, we are giving an overview of some of the top-running deep learning frameworks. This will help you zero in on the right option that will be perfect for your business.
Here are the top 8 deep learning frameworks that will accelerate your AI journey.
TensorFlow
Language – Python
Developed by – Google
License – Apache
If you have ever thanked Google Translate, you have experienced first-hand the capabilities of TensorFlow. TensorFlow comes with an array of deep learning capabilities mainly consisting of text classification/summarization, Natural Language Processing (NLP), speech/image/handwriting recognition, geo-tagging and much more. All these capabilities and many others have made it a first-choice for tech pioneers like IBM, Twitter, Airbus, among many others
Microsoft Cognitive Toolkit/CNTK
Language – C++/Python
Developed by – Microsoft
License – MIT
The CNTK or the Microsoft Cognitive Toolkit facilitates the efficient creation of convolution neural networks for data model training. Its USP compared to other frameworks is its high performance while operating on multiple machines. The only shortcoming though is its limited mobile-friendliness. But, its optimal use of resources makes it an efficient framework for implementing reinforcement learning models.
PyTorch
Language – Python
Developed by – Facebook
License – BSD
If you are an expert with Python, PyTorch will make you feel at home with creating deep learning networks. The deep learning framework has an improvised architectural style of Torch. Unlike Torch, it is not restricted by containers, which helps create data models quickly and transparently. PyTorch uses CUDA and C++ libraries for processing which helps build data models at scale and also with greater flexibility.
BigDL
Language – Scala
Developed by – Intel
License – Apache
BigDL is a distributed deep learning library for Apache Spark. Developers can write deep learning applications as Spark programs and launch them directly onto Spark or Hadoop clusters. The deep learning framework also allows uploading pre-trained Caffe or Torch models into Spark. BigDL is a fine choice for enterprises that have Big Data clusters that have to be analyzed on a real-time basis.
Caffe
Language – C++
Developed by – UC Berkeley
License –
Caffe is all about speed, modularity, and flexibility. It comes with a set of pre-trained networks from the Caffe Model Zoo which can be used to model Convolutional Neural Networks or image processing programs. Also, the deep learning framework boasts of openness with common code and models which can be reproduced easily for various deep learning projects. Also, there is a strong community where Caffe users share prototypes, academic research, etc. with community members.
MXNet
Language – Python/C++
Developed by – Apache
License – Apache
MXNet is an open-source deep learning library that helps you train and deploy deep learning frameworks. The framework allows developers to create deep learning models using several common programming languages like ++, Python, Julia, Matlab, JavaScript, Go, R, Scala, Perl and Wolfram Language. Also, users can import deep learning models exported from Open Neural Network Exchange (ONNX) and MXNet.
Chainer
Language – Python
Developed by – Preferred Networks
License – MIT
Chainer is a deep learning library that offers latest deep learning reinforcement algorithms to train and run neural networks. The intuitive framework also offers support for various network architectures including ConvNet, feed-forward nets, recursive nets and recurrent nets. Chainer MN, Chainer CV, and Chainer RL are the three extension libraries that come with the deep learning framework.
Eclipse Deeplearning4j
Language – Java
Developed by – Skymind
License – Apache
Eclipse Deeplearning4j is the first-of-its-kind commercial-grade, distributed a deep-learning library that comes integrated with Hadoop and Apache Spark. Being written in Java programming language, it works fine with any Java Virtual Machine languages like Kotlin, Scala or Clojure. Thanks to its Hadoop integration, Eclipse Deeplearning4j is quickly scalable. There is also GPU support for scaling on AWS making it a perfect fit for developing large dataset including machine learning processes like Robotic Process Automation, Recommender Systems, fraud detection and the likes.
Wrapping It Up
Deep learning is advancing the possibilities of artificial intelligence and machine learning. The layer based learning algorithm can bring about a radical change in applications where machines need to think on their feet like humans.
The choice of the deep learning framework upon which the system is built plays a major role. We have listed out of the top 8 deep learning frameworks which we think should help you accelerate your AI journey.
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