Thinking about diving into deep learning? About harnessing the power of AI and building your own text classification model? Or maybe your own face detection algorithm or product recommendation engine? Then the question that you're struggling with right now must be: “Which is the best deep learning framework for my particular use case and research area?”

What should you base your final choice on? 

• ease of use (a flexible architecture)?
• the efficiency of the code?
• freedom to write your own details for the implementation?
• supported languages?
• speed?
• support for a wide range of GPUs?
• community support?
• the variety of training materials and tutorials?
• convolutional neural network modeling capability?

Now, here's a shortlist of the 5 most popular deep learning frameworks!

Use it to narrow down your options and to identify that framework that perfectly fits your own project's requirements.

 

But First: Key Aspects to Consider When Evaluating these Frameworks

Before I go on with these key factors to take into consideration when weighing the strengths and limitations of these DL frameworks, let's get one thing straight:

There's no such thing as “the best deep learning framework”! Only “the most suitable” one for your specific DL project.

And this gets us to the particularities of your application of deep learning:

• What programming language will you be using?
• What type of neural network do you plan to develop?
• What's your budget?
• What/how many additional tools will you need?

With the list of answers to all these questions at hand, feel free to evaluate the following 5 most popular deep learning frameworks:

Just see if their capabilities match your project's specific requirements. As simple as that!

 

1. TensorFlow, The Best Deep Learning Framework for Complex DL Projects

No wonder that Tensorflow “reigns supreme” here, on top of the list: giants like AirBnB, Uber, and DeepMind make the most of its capabilities!

Now, a succinct definition would go something like this:

A Google-powered deep learning framework written in C++ and Python.

“And what are the use cases that it excels at?” you might ask yourself.

Here are some of its best applications:

• for building multiplayer neural networks
• … text-based apps (e.g. Google Translate)
• … image/voice recognition apps

In short: TensorFlow rocks at powering complex, large projects, cross-platform deployments.

 

Significant Advantages:

• CNN modeling capability
• an active community of developers and tech companies supporting it
• a rich variety of documentation and training materials available for newbies
• it provides model serving
• modular front-end

Main Disadvantages:

• lower speed compared to other DL frameworks 
• debugging can get quite challenging (due to its default “define and run” mode)
• it supports one single language: Python 
• it can be intimidating for newcomers; it's not the quickest way to start with deep learning
   

2. Keras

A minimalistic neural network written in Python that can be run on top of CNTK, TensorFlow or Theano. One supporting various types of layers:

• recurrent layers
• convolutional layers
• dense layers

The main purpose that it serves?

Prototyping and rapid experimentation. 

It's been designed to help developers get the prototypes of their neural network up and running – working with TensorFlow – in no time.

Now, when it comes to its best use cases:

• tagging and translation
• generating, classifying, translating, and summarizing text 
• image/speech recognition
   

Significant advantages:

• a minimalist and intuitive interface enabling quick prototyping
• it's conveniently lightweight, for those use cases when you need to develop multi-layered DL models
• it makes prototyping incredibly fast
• easy to use
• built-in support for training of various GPUs
• supports Open-CL-enabled GPUs, Google TPUs, NVIDIA GPUs
• fully-configurable modules
• can be trained on Google Cloud, on clusters of GPUs

Main disadvantages:

• customizing it might get challenging
• constrained to Theano, TensorFlow and CNTK back-ends
• it provides less control over the network
• has fewer functionalities compared to TensorFlow

3. PyTorch

If TensorFlow's the best deep learning framework for many, then Pytorch here is its main “rival”.

As compared to its predecessor, Lua-based Torch, Pytorch runs on Python. Moreover, if uses C/C++ libraries and CUDA for processing.

In other words: any developer with some kind of knowledge of Python can start building his/her own deep learning model.

Should I also add that it's Facebook-powered and that some of its “famous” users are: the University of Oxford, Twitter, and Salesforce?

“What purpose does it serve?”

To scale the production of building deep learning models. That is: to make the whole process of training models a lot quicker, more effective, more... flexible.

No wonder that PyTorch makes the go-to deep learning framework for a lot of researchers.

 

Significant advantages:

• a simple and transparent deep modeling process
• declarative data parallelism
• stands out as the fastest DL framework (there's minimal framework overhead to impact its performance)
• comes with lots of pre-trained models and modular components that you get to mix and match
• its “define by run” mode resembles traditional programming; you get to use standard debugging tools (PyCharm debugger, pdb, ipdb...)
   

Main disadvantages:

• no model serving
• since it's relatively new, it's safer for you to use it for less complex projects or prototyping
• it doesn't provide an interface for monitoring and visualization (though you can still integrate it, externally, with Tensorboard)
   

4. Caffe and Caffe 2

A deep learning framework implemented in C++, that comes with a useful Python interface.

Its biggest strength?

It excels at modeling CNNs (convolutional neural networks), handling image processing issues and improving existing networks.

In short: Caffe is the go-to framework for visual recognition...

Popular for its transportability and high speed, Caffe accesses the available networks in the Caffe Model Zoo deep net repository. Networks which are pre-trained and ready-to-be-used.

As for Caffe 2, it's a modular, lightweight and conveniently scalable DL framework (with C++ APIs and native Python), perfect for:

• large-scale deployments in production environments
• mobile projects

A more flexible upgraded version of Caffe...

Significant advantages:

• provides pre-trained models (for putting together demo apps)
• scalable, lightweight and therefore much faster than its main “competitors”
• server-optimized inference
• “plays well” with other deep learning frameworks (e.g. PyTorch)
   

Main disadvantages: 

• not as well documented as other frameworks
• compiling it can be difficult
• poor support for language modeling and recurrent networks
• rather low community support
• doesn't support multi-layered networks (like TensorFlow, for instance)
   

Caffe 2 is still relatively new. Not yet the go-to choice for implementing complex networks (that would call for an active community's support, and reliable debugging tools).

5. Theano

Back in 2007, when it was developed, Theano used to be the best deep learning framework.

That because, at that time, it was the only framework available.

Now, although it has lost much of its popularity (and its development has ceased), this Python-based library still is a powerful tool for handling numerical operations in a highly efficient way.

Operations that call for multi-dimensional arrays.

 

Significant advantages:

• highly efficient for numerical tasks
• ideally optimized for GPU and CPU
   

Main disadvantages:

• rather buggy on AWS
• Raw Theano is quite low-level compared to other libraries
• for reaching a higher level of abstraction you need to pair it with other libraries
   

The END!

With this shortlist at hand, time to choose the best deep learning framework for your project. For your own use case, requirements and specific needs...

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