Top 10 Programming Languages to Learn in 2019


Top 10 Deep Learning frameworks in 2019 (with comparison)

Artificial Intelligence is the future of the programming world. More and more developers, seeing the growing demand for AI technologies, familiarize themselves with this science. And when you start learning AI and how it can be implemented in programming, the first question which comes to mind is “What are the best languages/frameworks/libraries to use?” That’s exactly what we will cover today in this review of the top 10 Deep Learning frameworks and libraries every programmer must know.

While the majority of us are fascinated by the early applications of machine learning, it continues to evolve at quite a promising pace, introducing us to more advanced algorithms like Deep Learning. This branch, by the way, is attracting even more attention than all other ML-algorithms combined. Of course, I don’t have to declare it. It’s here.

And let’s be honest with ourselves that most of the languages today are not compatible with ML/AI. Take the example of Ruby, most developers are switching to Python only due to the former’s inability for Machine Learning. So let’s go through our researched and compiled list of Top 10 Deep Learning frameworks in 2019.

Why Deep Learning frameworks are important?

Consider this image. You have a task to classify every animal in the image.

top 10 deep learning framework -animals list
animals list

You can approach the problem in two different ways:

  1. Implement Convolutional Neural Network from scratch.. In other words reinvent the wheel..
  2. Or, use a framework which already has the boiler plate code for you, all you will have to do is to properly implement it.

Well, either way you will be able to reach the goal, just the former will take more time, and the latter is faster and easier.

This is where deep learning frameworks have truly changed the landscape.

Instead of writing hundreds of lines of code, we can use a suitable framework to help us to build such a model quickly. Below are some of the key features of a good deep learning framework:

  1. Optimized for performance
  2. Easy to understand and code
  3. Good community support
  4. Mostly Used for which purpose
  5. Parallelize the processes to reduce computations
  6. Automatically compute gradients

These are the criteria we used to pick out our Top 10 deep learning frameworks. Let’s dive into each of them in detail.



Google’s Tensorflow — arguably the most popular Deep Learning framework today. Gmail, Uber, Airbnb, Nvidia and lots of other prominent brands using it. TF is mainstream and number one DL frame today, but frankly speaking, it’s a rare case when popularity equals effectiveness.

TensorFlow was developed by researchers and engineers from the Google Brain team. It is far and away from the most commonly used software library in the field of deep learning (though others are catching up quickly).

Key Things to Know for TensorFlow:

  1. TensorFlow is completely Open-Sourced
  2. TensorFlow as most of the complex Neural Network’s code like RNN has been already written in it.
  3. It has a very huge community which is helping in its development.
  4. Python is the most convenient client language for working with TensorFlow. However, there are also experimental interfaces available in JavaScript, C ++, Java and Go, C # and Julia.
    TF takes into account not only powerful computing clusters but also the ability to run models on mobile platforms like iOS and Android.
  5. TF needs a lot of coding. It will not give you powerful AI overnight, it’s just a tool for deep learning research that will hopefully make it a bit less cumbersome.
  6. TensorFlow operates with a static computation graph. That is, we first define the graph, then we run the calculations and, if we need to make changes to the architecture, we re-train the model.

One of the biggest reasons TensorFlow is so popular is its support for multiple languages to create deep learning models, such as Python, C++ and R. It has proper documentation and walkthroughs for guidance.

Use Cases:

The flexible architecture of TensorFlow enables us to deploy our deep learning models on one or more CPUs (as well as GPUs). Below are a few popular use cases of TensorFlow:

  • Text-based applications: Language detection, text summarization
  • Image recognition: Image captioning, face recognition, object detection
  • Sound recognition
  • Time series analysis
  • Video analysis

You can install it by one line of code:

pip install tensorflow



Remember when we said TensorFlow is the most commonly used deep learning framework right now? It might not hold that mantle for too long given the rapid pace with which data scientists and developers are embracing Facebook’s PyTorch.

The PyTorch framework was developed for Facebook services but is already used for its own tasks by companies like Twitter and Salesforce.

Key Things to Know for PyTorch:

  • Unlike TensorFlow, the PyTorch library operates with a dynamically updated graph. This means that it allows you to make changes to the architecture in the process.
  • In PyTorch, you can use standard debuggers, for example, pdb or PyCharm.

What Is It Good For?

  • The process of training a neural network is simple and clear. At the same time, PyTorch supports the data parallelism and distributed learning model, and also contains many pre-trained models.
  • PyTorch is much better suited for small projects and prototyping. When it comes to cross-platform solutions, TensorFlow looks like a more suitable choice. However, it is worth noting that for the same tasks, the Caffe2 mobile framework introduced in 2017 can be used.

You can work on all sorts of deep learning challenges using PyTorch, including:

  1. Images (Detection, Classification, etc.)
  2. Text (NLP)
  3. Reinforcement Learning

If you’re wondering how to install PyTorch on your machine, hold on for a moment. The installation steps vary depending on your operating system, the package you want to use to install PyTorch, the tool/language you’re working with, CUDA and a few other dependencies.



Let me summarize this framework for you in one line.. “If you like Python you will surely love keras”.

Keras is a machine learning framework that might be your new best friend if you have a lot of data and/or you’re after the state-of-the-art in AI: deep learning. Plus, it’s the most minimalist approach to using TensorFlow, Theano, or CNTK is the high-level Keras shell.

Keras can run on top of TensorFlow (as well as CNTK and Theano). The TensorFlow interface can be a bit challenging as it is a low-level library and new users might find it difficult to understand certain implementations.

Keras, on the other hand, is a high-level API, developed with a focus to enable fast experimentation. So if want quick results, Keras will automatically take care of the core tasks and generate the output. Both Convolutional Neural Networks and Recurrent Neural Networks are supported by Keras. It runs seamlessly on CPUs as well as GPUs.

Key Things to Know:

  • Keras is usable as a high-level API on top of other popular lower level libraries such as Theano and CNTK in addition to Tensorflow.
  • Prototyping here is facilitated to the limit. Creating massive models of deep learning in Keras is reduced to single-line functions.

What Is It Good For?

  • Keras is the best Deep Learning framework for those who are just starting out. It’s ideal for learning and prototyping simple concepts, to understand the very essence of the various models and processes of their learning.
  • Keras model Serialization/Deserialization APIs, callbacks, and data streaming using Python generators are very mature.
  • It is a beautifully written API. The functional nature of the API helps you completely and gets out of your way for more exotic applications.
  • Keras results in a much more readable and succinct code.

Keras has multiple architectures, mentioned below, for solving a wide variety of problems. This includes one of my all-time favorites – image classification!

  1. VGG16
  2. VGG19
  3. InceptionV3
  4. Mobilenet, and many more

You can refer to the official Keras documentation to get a detailed understanding of how the framework works.

You can install Keras with just one line of code:

pip install keras



MXNet is a highly scalable deep learning tool that can be used on a wide variety of devices. Although it does not appear to be as widely used as yet compared to TensorFlow, MXNet growth likely will be boosted by becoming an Apache project.

Key Things to Know for MXNet:

  • The framework initially supports a large number of languages (C ++, Python, R, Julia, JavaScript, Scala, Go, and even Perl).
  • The main emphasis is placed on the fact that the framework is very effectively parallel on multiple GPUs and many machines. This, in particular, has been demonstrated by his work on Amazon Web Services.

What Is It Good For?

  • Support of multiple GPUs (with optimized computations and fast context switching)
  • Clean and easily maintainable code (Python, R, Scala, and other APIs)
  • Fast problem-solving ability (vital, for newbies in deep learning, like me)

Although it is not so popular as TF, MXNet has detailed documentation and is easy to use, with the ability to choose between imperative and symbolic programming styles, making it a great candidate for both beginners and experienced engineers.

If you interested in learning more about MXNet they also offer a 60 mins crash course to get you started.



Gluon is one more great Deep Learning framework that can be used to create simple as wells as sophisticated models.

Key Things to Know:

  • The specificity of the Gluon project is a flexible interface that simplifies prototyping, building and training deep learning models without sacrificing learning speed.
  • Gluon is based on MXNet and offers a simple API that simplifies the creation of deep learning models.
  • Similar to PyTorch, the Gluon framework supports work with a dynamic graph, combining this with high-performance MXNet. From this perspective, Gluon looks like an extremely interesting alternative to Keras for distributed computing.

What Is It Good For?

  • In Gluon, you can define neural networks using the simple, clear, and concise code.
  • It brings together the training algorithm and neural network model, thus providing flexibility in the development process without sacrificing performance.
  • Gluon enables to define neural network models that are dynamic, meaning they can be built on the fly, with any structure, and using any of Python’s native control flow.



Sonnet deep learning framework built on top of TensorFlow. It is designed to create neural networks with a complex architecture by the world famous company DeepMind.

Key Things to Know:

  • High-level object-oriented libraries that bring about abstraction when developing neural networks (NN) or other machine learning (ML) algorithms.
  • The idea of Sonnet is to construct the primary Python objects corresponding to a specific part of the neural network. Further, these objects are independently connected to the computational TensorFlow graph. Separating the process of creating objects and associating them with a graph simplifies the design of high-level architectures. More information about these principles can be found in the framework documentation.

What Is It Good For?

  • The main advantage of Sonnet, is you can use it to reproduce the research demonstrated in DeepMind’s papers with greater ease than Keras, since DeepMind will be using Sonnet themselves.

So all-in-all, it’s a flexible functional abstractions tool that is absolutely a worthy opponent for TF and PyTorch.



Deeplearning4j is implemented in Java and is hence more efficient as compared to Python. It uses the tensor library called ND4J which provides an ability to work with n-dimensional arrays (also called tensors). This framework also supports both CPUs and GPUs.

DL4j treats the task of loading data and training algorithms as separate processes. This separation of functions provides a whole lot of flexibility. And who wouldn’t like that, especially in deep learning?!

Key Things to Know:

  • Training of neural networks in DL4J is carried out in parallel through iterations through clusters.
  • The process is supported by Hadoop and Spark architectures.
  • Using Java allows you to use the library in the development cycle of programs for Android devices.

What Is It Good For?

  • A very good platform if you are looking for a good Deep Learning Framework in Java.

The kind of deep learning models you can build using Deeplearning4j are:

  1. Convolutional Neural Networks (CNNs)
  2. Recurrent Neural Networks (RNNs)
  3. Long Short-Term Memory (LSTM) and many other architectures.



The ONNX project was born from the collaboration of Microsoft and Facebook as a search for an open format for the presentation of deep learning models. ONNX simplifies the process of transferring models between different means of working with artificial intelligence. Thus, ONNX allows you to consider the benefits of various Deep Learning frameworks.

Key Things to Know:

  • ONNX enables models to be trained in one framework and transferred to another for inference. ONNX models are currently supported in Caffe2, Microsoft Cognitive Toolkit, MXNet, and PyTorch, and there are connectors for many other common frameworks and libraries.

What Is It Good For?

  • ONNX is a piece of good news for PyTorch developers. But, for those who prefer to work with TensorFlow, Keras, etc. might have to wait for a little.


Until the advent of DyNet at CMU, and PyTorch at Facebook, Chainer was the leading neural network framework for dynamic computation graphs or nets that allowed for input of varying length, a popular feature for NLP tasks.

Key Things to Know:

  • The code is written in pure Python on top of the Numpy and CuPy libraries. Chainer is the first framework to use a dynamic architecture model (as in PyTorch).
  • Chainer several times beat records on the effectiveness of scaling when modelling problems solved by neural networks.

What Is It Good For?

  • By its own benchmarks, Chainer is notably faster than other Python-oriented frameworks, with TensorFlow the slowest of a test group that includes MxNet and CNTK.
  • Better GPU & GPU data center performance than TensorFlow. (TensorFlow is optimized for TPU architecture) Recently, Chainer became the world champion for GPU data center performance.
  • Good Japanese support.
  • OOP like programming style.


Originally created as a PhD project by Yangqing Jia (he works at Facebook now) at Berkeley, Caffe has become one the most popular machine learning frameworks out there. You should definitely check out their image classification demo. It is not perfect yet but at least it’s capable of recognizing dogs and cats:)

First, a caveat – Caffe’s support for recurrent networks and language modelling is not as great as the above three frameworks. But where Caffe stands out is it’s speed of processing and learning from images. That is easily it’s primary USP.

Caffe can process over sixty million images on a daily basis with a single NVIDIA K40 GPU. That’s 1 ms/image for inference and 4 ms/image for learning.

The Caffe Model Zoo framework allows us to access pretrained networks, models and weights that can be applied to solve deep learning problems. These models work on the below tasks:

  1. Simple regression
  2. Large-scale visual classification
  3. Siamese networks for image similarity
  4. Speech and robotics applications


Deep Learning FrameworkRelease YearWritten in which language?CUDA supported?Does it have pretrained models?
TensorFlow2015C++, PythonYesYes
PyTorch2016Python, CYesYes
Deeplearning4j2014C++, JavaYesYes
ONNX2017Python, C++
MXNet2015C++, PythonYesYes

It’s a pretty handy table for the next time you’re working with these frameworks!

All of these frameworks are open source, support CUDA and have pretrained models to help you get started. But, what should be the right starting point and which framework should you choose to build your (initial) deep learning models? Let’s discuss!

So, Which Deep Learning Framework Should You Use?

Well this is what I analyzed and found out:

  • If you are just starting out and want to figure out what’s what, the best choice is Keras.
  • For research purposes, choose PyTorch.
  • For production, you need to focus on the environment. So, for Google Cloud, the best choice is TensorFlow, for AWS — MXNet and Gluon.
  • Android developers should pay attention to D4LJ, for iOS, a similar range of tasks is compromised by Core ML.
  • Finally, ONNX will help with questions of interaction between different frameworks.

Artificial Intelligence is a very attractive niche in the programming world. Any self-respecting software development company must already have a dedicated AI development team. However, becoming a successful AI specialist requires intelligence, talent, patience, and hard work. If you have those then now is the best time to get into this field.

Provided you already know some fundamental programming language such as Python and the basics of machine learning, all you have to do is to select the right AI framework and get to work.

End Notes

Are there any other deep learning frameworks you’ve worked on? I would love to hear your thoughts and feedback on that plus the ones we covered in this article. Connect with me in the comments section below.

And remember, these frameworks are essentially just tools that help us get to the end goal. Choosing them wisely can reduce a lot of effort and time.

You can also check out our other awesome articles:

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