Top 5 Deep Learning Frameworks for 2019
Deep learning is a very important aspect of machine learning that focuses on solving the most complex problems in a short time. Most businesses are looking to increase their operations and to do so, they need different frameworks to do so.
There are a lot of deep learning frameworks available in the market today. Each of them was developed keeping in mind a specific set of functions.
We’ll compare 5 of the most useful and popular deep learning frameworks and then compare them based on their userbase, efficiency, community support, ease of control and then reaching some kind of conclusion based on that.
It was first developed by Google in 2015 and later placed as an opensource for the rest of the world. The most widespread use of Tensorflow in Google is in Google Translate.
One of the most flexible frameworks around the market, Tensorflow is available on both desktop and mobile.
Supported languages – Python, R, C++as well as their casing library
Natural language processing, text classification, speech recognition, image recognition, handwriting recognition, forecasting, tagging
Major companies using Tensorflow
PyTorch is a scientific computational framework and has been developed by Facebook for the first time. It is Lua-powered and has been somewhat utilized by some giants like Facebook, Twitter and even Google.
Pytorch’s first major use was in automatic pop-ups, which lets you automatically tag people in images uploaded to your Facebook, due to its high facial recognition applications.
The adaptation of Pytorch in deep learning communities is growing day by day and it is giving a hard hit to the tensorflow.
Languages supported – Python
PyTorch runs entirely on Python, so even a basic understanding of Python can start codeing their own learning models.
Declarative Data Parallelism, Pretrained Models, Modular Parts, Distributed Learning
Keras uses neural network structures which are widely known to be the simplest and easiest. As we discussed the need for very low level programming in Tensorflow, the prototype required a more minimalistic method to create a neural network model.
It is to be noted that Keras is an API and not a standalone library like Tensorflow.
More than 4800 contributors combined to develop this framework. It currently has an active developer base of over 250,000 with a 2-fold increase every year.
Kerus is coded in Python and supports both CNN (Conventional Neural Networks) and RNN (Reichert Neural Networks). Kerus models can run on top of tensorflow, CNTK or the theno.
Languages supported – Python
Classification, text generation, text summing, modular parts, learning distribution
MXnet (pronounced as a mixnet) is the reference library of Amazon for deep learning. It is widely adopted in AWS by Amazon.
Built specifically for high efficiency, productivity, flexibility and scalability, MXNet offers you the simplicity of a framework like Kearas that has the functionality of a more advanced framework which makes debugging much easier.
One of the other important aspects is that it supports all major programming languages like R, Python, Scala, C++ and Julia. If you are a developer with proficiency in any of these languages, you won’t need to learn anything new and can just start building your own intensive learning model using MXnet.
Supported languages – R, Scala, Python, Julia, C++
Image classification, forecasting, NLP, handwriting recognition, speech recognition
5. Caffe and Caffe 2
It is a deep learning framework that is mostly known for its image processing capabilities. The main advantage of using Kaif is its speed and scalability in processing images. Caffe can process 60 million images per day on 40 GPU of single Nvidia.
It is very useful because of its deep learning access to the library called Caffe Model Zoo, which has already trained models that can be used directly.
The documentation of Caffe is not very good and, therefore, it has not been posted by the major companies.
Facebook has launched Caffe 2 in 2017, which is mainly focused on mobile work and mass operations in production scale.
Supported languages – C, C++, Python, Matlab, Command Line
Image classification, image processing, visual recognition