The Artificial Intelligence (AI) sector is rapidly growing with algorithms developing to meet and even exceed human capabilities. One awesome example is Deep Learning (DL), and emerging machine learning subfield which can continue to evolve on its own, without the need for continued programming. When companies want to use AI to expand and to get their startup to take off, one aspect is essential: the technology with which they choose to operate must be combined with an appropriate deep learning framework, particularly since each framework serves a specific purpose. In terms of smooth and quick business development, as well as efficient delivery, finding the perfect fit is not only important but also necessary.

Given that deep learning is the key to performing tasks of a higher level of complexity and logical thinking, successfully building and deploying them proves to be quite a difficult challenge for data scientists and data engineers worldwide. Today we have a multitude of frameworks that enable us to develop tools that can offer a better level of precision along with simplifying the challenging programming. Each platform is designed to benefit various purposes differently.

Here, we look at some of the top 5 Best deep learning frameworks to give you a better understanding of which framework can fit perfectly or be useful in solving your business tasks.

TensorFlow

TensorFlow, developed by Google and written in C++ and Python, is considered as one of the best open-source resources for numerical computing. Particularly if giants like Uber, DeepMind, AirBnB, or Dropbox have all chosen to exploit the system, we don’t need any other proof of its greatness. TensorFlow is ideal for advanced projects such as building neural networks with multilayers. It’s used for voice and image recognition and text-based applications (such as Google Translate).

Pros:

  • Extensive documentation and help
  • Monitoring and visualization of models
  • On-device inference for mobile using TensorFlow Lite
  • Model serving
  • Distributed training

Cons:

  • Low speed compared to other frameworks
  • Learning and debugging can be difficult for beginners

Keras

Keras is an open-source neural network library developed in Python. It provides compatibility with TensorFlow, R, Microsoft Cognitive Toolkit, or Theano. Designed by Francois Chollet, a Google engineer, to allow rapid experimentation with deep neural networks, it is user-friendly, scalable, and extensible. It is a minimalistic library based on Python that can run on top of TensorFlow, Theano or CNTK.

Pros:

  • Minimalistic and easy to use
  • Large and helpful community
  • Easy backend services
  • Awesome compatibility with well-known frameworks

Cons:

  • Limited customization
  • Constrained to Tensorflow, CNTK, and Theano backends

PyTorch

PyTorch is fairly new but is becoming increasingly popular. It is also open-source, built primarily by Facebook, and is renowned for its usability, versatility, and customizability. PyTorch is designed with a clean architectural model which makes it easy to learn and execute the training process and create deep learning models. Py stands for Python so anyone with a basic understanding of Python can start developing and training their deep learning models.

Pros:

  • The define-by-run mode is just like the simple programming
  • Easy debugging with common tools
  • Provides declarative data parallelism
  • Contains a lot of pre-trained parts
  • Production-ready

Cons:

  • A bit immature as with all new technologies

Caffe

Caffe is a deep learning platform, supported by frameworks such as C, C++, Python, MATLAB, and the CLI. It is well known for its transposability, speed, and applicability in Convolution Neural Networks (CNN) modeling. The biggest advantage of using the C++ library of Caffe (comes with a Python interface) is accessing available networks from the ‘Caffe Model Zoo’ deep net repository that are pre-trained and can be used instantly. Whether modeling CNNs or solving issues related to image processing, this is your perfect library.

Pros:

  • The biggest selling point is its speed, measured at 1 ms/image for inference and 4 ms/image for learning
  • Offers server optimized inference
  • Fast, scalable, and lightweight

Cons:

  • No support for fine granularity network layers

MXNet

MXNet (pronounced mix-net) is a deep learning platform that is powered by Python, R, C++, and Julia, specifically designed for higher efficiency, productivity and versatility. MXNet’s strength is that it allows the user to code in several programming languages (Python, C++, R, Julia, and Scala, to name a few). This means you can train your models of deep learning with whatever language you’re comfortable in without having to learn anything new. This framework has been used by Amazon as its reference library for deep learning.

Pros:

  • Can scale and work with multiple GPUs
  • Long Short-Term Memory (LTSM) networks with both RNN and CNN
  • A high-performance imperative API
  • Easy Model serving

Cons:

  • Much smaller community
  • Less popular among researchers

Conclusion

Given above are some of the best deep learning frameworks available in the market. Compare your product’s requirements with the pros and cons of the platforms and you can easily see which one you should be using for your project.