Today’s guest post is all about choosing the best deep learning framework. To facilitate this, our writer has gone into a brief introduction on what deep learning is, and then has provided a review for the top 5 best deep learning framework on the market today.

Artificial intelligence (AI) and machine learning are driving a new form of analytics called deep learning, which is, in itself, a form of data science. By mimicking the neural activity that takes place within the human brain and forms the basis of learning in the real world, such technologies can also be taught through digital data and media. In a way, this trains AI to dig much deeper into data and information stores. It results in newer, more relevant insights and information, which can be leveraged quickly.

Choosing the Best Deep Learning Framework… Reviewing the Top 5

Many tech companies, from Google and Amazon to IBM, have developed their own neural network or deep learning frameworks systems. They serve as frameworks for your own processes and systems, allowing you to deploy and merge your projects with the aforementioned technologies. It’s a highly competitive market that’s only going to increase as time goes on and more platforms appear.

Which ones are the best, at least right now? If you were to choose one or two, which would be ideal for your upcoming project?

Google’s Tensor Flow

Tensor Flow was launched under the Apache 2.0 license on Nov. 9, 2015, and almost immediately it gained an incredibly loyal following from the community. Part of this is because it was developed from a previous library called DistBelief V2.

More importantly, though, TensorFlow supported a much broader range of capabilities and features at launch that weren’t available for previous platforms and applications. This included image, handwriting and speech recognition support, pattern and trend forecasting, and natural language processing.

As far are picking the best deep learning framework, you should know that Tensor Flow is among the most commonly used deep learning frameworks today.

TensorFlow can be broken down into two major tools: TensorBoard and TensorFlow Serving. TensorBoard is primarily used for network modeling and performance visualizations. Serving allows teams to leverage the TensorFlow deep learning platform while applying the technology via experimental scenarios and to external models and data.

Most of the open source code is written in Python and C++, but later versions currently support — or will support — Java, GO, R and the Haskell API.

Sadly, it’s not perfect. Performance can be questionable at times, despite major improvements. It also lacks 3-D convolution, which can be incredibly helpful for video and media recognition projects.


Theano was one of the first deep learning platforms. It was originally developed by Yoshua Bengio and the University of Montreal research team.

It’s comprised of a Python library that is both fast and powerful, especially for its time. However, many feel that it is a low-level deep learning framework with little room for growth. It was promising, but the Theano team announced it was deprecated in 2017, with 1.0 being the final release; Since you’re looking to choose the best deep learning framework and Theano is being deprecated – you won’t want to pick this one.

It paved the way for offerings from Google and Microsoft and serves as the inspiration or basis for many other deep learning libraries and platforms.

Caffe and Caffe 2

Caffe was developed by Yangquing Jia, who is now a lead engineer for Facebook’s AI platform. Jia and his team at Facebook have moved on to Caffe 2, which is remarkably similar. The newest iteration is designed for large-scale deployments and modular use cases.

Caffe was one of the first deep learning toolkits — in the mainstream sense — to offer industry-level support options and features. What makes it so popular and viable even today is the speed and performance it offers. Just a single Nvidia K40 GPU can be used to process over 60 million images per day using the framework.

If you are looking for the best deep learning framework for image- and vision-recognition projects, Caffe may just be the optimal choice. After all, that’s what this package was specifically designed for. Unlike TensorFlow and Theano, Caffe does not include support for fine-granularity network layers, which means it’s not so great for network and language modeling.

Caffe 2, on the other hand, will soon overtake the original framework and includes support for more advanced deployments, such as natural language processing, handwriting recognition and time series forecasting.
CNTK or Microsoft Cognitive Toolkit

Microsoft’s deep neural network was designed for speech recognition and future improvements of the technology. It can also handle handwriting and speech recognition.

Like TensorFlow and Theano, Microsoft’s platform relies on graph- and vector-based operations, like a matrix add and multiply function. Like its brethren, CNTK also works with fine granularity when building network layers and similar deployments. The result is the benefit of more complex layer types, without the need for a low-level language — which happens with Caffe.

As with most Microsoft platforms, CNTK is based in C++ and includes cross-platform support for CPU and GPU deployments. Microsoft offers a version of CNTK powered by the Azure cloud GPU Lab, which offers considerable performance improvements.

The biggest downside of working with CNTK is that it lacks ARM architecture support, which is common on mobile platforms. This can limit use of the technology for mobile projects and can be quite the hindrance.

Torch and Pytorch

Torch was developed by a vast group of influential people, including Ronan Collobert and Soumith Chintala from Facebook, Clement Farabet from Twitter, and later Nvidia Corp. and Koray Kavukcuoglu from Google.

For quite some time, Facebook, Twitter and Nvidia have been the major contributors to the platform. More recently, Facebook announced its plans to focus on Caffe 2 instead. Pytorch, a Python version of the Torch framework, is also the brainchild of Facebook’s team.

Both variants of Torch offer high-speed computations and incredible flexibility. This is thanks to the support of the Lua language, which is not mainstream compared to so many others. C++ and Python, for comparison, are commonly taught. It is worth noting that Lua makes it ridiculously easy to build and develop models.

By far, Torch is the best deep learning framework for people who have Lua capabilities. Once a team is well-versed in Lua, its true benefits start to shine — and that’s why it earned a spot on this list. The downside is that there is a lack of language APIs for compatibility with additional, more common languages, which severely limits the framework’s audience.

Deep Learning Has a Bright Future Ahead

While the deep learning frameworks listed here are some of the best, they are not the only common and useful platforms to consider. Some runner-ups include Deeplearning4J, MXNet and Keras.

The technology will continue to mature and improve long into the future, which means you’ll likely see more platforms crop up, or even some of the ones listed here lose grace with the community. It’s important to choose a platform that’s the most viable and relevant to your business and operations. Also, consider the requirements for using each platform and how that factors into your overall timeline. When you find the right match, your project will reap the benefits.

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