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Deep learning frameworks are powering the artificial intelligence revolution. Without them, it would be almost impossible for data scientists to deliver the level of sophistication in their deep learning algorithms that advances in computing and processing power have made possible. Put simply, deep learning frameworks make it easier to build deep learning algorithms of considerable complexity. This follows a wider trend that you can see in other fields of programming and software engineering; open source communities are continually are developing new tools that simplify difficult tasks and minimize arduous ones.

The deep learning framework you choose to use is ultimately down to what you’re trying to do and how you work already. But to get you started here is a list of 10 of the best and most popular deep learning frameworks being used today.

What are the best deep learning frameworks?


One of the most popular Deep Learning libraries out there, Tensorflow, was developed by the Google Brain team and open-sourced in 2015. Positioned as a ‘second-generation machine learning system’, Tensorflow is a Python-based library capable of running on multiple CPUs and GPUs. It is available on all platforms, desktop, and mobile. It also has support for other languages such as C++ and R and can be used directly to create deep learning models, or by using wrapper libraries (for e.g. Keras) on top of it.

In November 2017, Tensorflow announced a developer preview for Tensorflow Lite, a lightweight machine learning solution for mobile and embedded devices. The machine learning paradigm is continuously evolving – and the focus is now slowly shifting towards developing machine learning models that run on mobile and portable devices in order to make the applications smarter and more intelligent.

Learn how to build a neural network with TensorFlow.

If you’re just starting out with deep learning, TensorFlow is THE go-to framework. It’s Python-based, backed by Google, has a very good documentation, and there are tons of tutorials and videos available on the internet to guide you. You can check out Packt’s TensorFlow catalog here.


Although TensorFlow is a very good deep learning library, creating models using only Tensorflow can be a challenge, as it is a pretty low-level library and can be quite complex to use for a beginner. To tackle this challenge, Keras was built as a simplified interface for building efficient neural networks in just a few lines of code and it can be configured to work on top of TensorFlow. Written in Python, Keras is very lightweight, easy to use, and pretty straightforward to learn. Because of these reasons, Tensorflow has incorporated Keras as part of its core API.

Despite being a relatively new library, Keras has a very good documentation in place. If you want to know more about how Keras solves your deep learning problems, this interview by our best-selling author Sujit Pal should help you.

Read now: Why you should use Keras for deep learning

[box type=”success” align=”” class=”” width=””]If you have some knowledge of Python programming and want to get started with deep learning, this is one library you definitely want to check out![/box]


Built with expression, speed, and modularity in mind, Caffe is one of the first deep learning libraries developed mainly by Berkeley Vision and Learning Center (BVLC). It is a C++ library which also has a Python interface and finds its primary application in modeling Convolutional Neural Networks. One of the major benefits of using this library is that you can get a number of pre-trained networks directly from the Caffe Model Zoo, available for immediate use. If you’re interested in modeling CNNs or solve your image processing problems, you might want to consider this library.

Following the footsteps of Caffe, Facebook also recently open-sourced Caffe2, a new light-weight, modular deep learning framework which offers greater flexibility for building high-performance deep learning models.


Torch is a Lua-based deep learning framework and has been used and developed by big players such as Facebook, Twitter and Google. It makes use of the C/C++ libraries as well as CUDA for GPU processing.  Torch was built with an aim to achieve maximum flexibility and make the process of building your models extremely simple. More recently, the Python implementation of Torch, called PyTorch, has found popularity and is gaining rapid adoption.


PyTorch is a Python package for building deep neural networks and performing complex tensor computations. While Torch uses Lua, PyTorch leverages the rising popularity of Python, to allow anyone with some basic Python programming language to get started with deep learning. PyTorch improves upon Torch’s architectural style and does not have any support for containers – which makes the entire deep modeling process easier and transparent to you.

Still wondering how PyTorch and Torch are different from each other? Make sure you check out this interesting post on Quora.


DeepLearning4j (or DL4J) is a popular deep learning framework developed in Java and supports other JVM languages as well. It is very slick and is very widely used as a commercial, industry-focused distributed deep learning platform. The advantage of using DL4j is that you can bring together the power of the whole Java ecosystem to perform efficient deep learning, as it can be implemented on top of the popular Big Data tools such as Apache Hadoop and Apache Spark.

[box type=”success” align=”” class=”” width=””]If Java is your programming language of choice, then you should definitely check out this framework. It is clean, enterprise-ready, and highly effective. If you’re planning to deploy your deep learning models to production, this tool can certainly be of great worth![/box]


MXNet is one of the most languages-supported deep learning frameworks, with support for languages such as R, Python, C++ and Julia. This is helpful because if you know any of these languages, you won’t need to step out of your comfort zone at all, to train your deep learning models. Its backend is written in C++ and cuda, and is able to manage its own memory like Theano. MXNet is also popular because it scales very well and is able to work with multiple GPUs and computers, which makes it very useful for the enterprises. This is also one of the reasons why Amazon made MXNet its reference library for Deep Learning too. In November, AWS announced the availability of ONNX-MXNet, which is an open source Python package to import ONNX (Open Neural Network Exchange) deep learning models into Apache MXNet.

Read why MXNet is a versatile deep learning framework here.

Microsoft Cognitive Toolkit

Microsoft Cognitive Toolkit, previously known by its acronym CNTK, is an open-source deep learning toolkit to train deep learning models. It is highly optimized and has support for languages such as Python and C++. Known for its efficient resource utilization, you can easily implement efficient Reinforcement Learning models or Generative Adversarial Networks (GANs) using the Cognitive Toolkit. It is designed to achieve high scalability and performance and is known to provide high-performance gains when compared to other toolkits like Theano and Tensorflow when running on multiple machines.

Here is a fun comparison of TensorFlow versus CNTK, if you would like to know more.


Gone are the days when you required serious hardware to run your complex machine learning models. With deeplearn.js, you can now train neural network models right on your browser! Originally developed by the Google Brain team, deeplearn.js is an open-source, JavaScript-based deep learning library which runs on both WebGL 1.0 and WebGL 2.0.

deeplearn.js is being used today for a variety of purposes – from education and research to training high-performance deep learning models. You can also run your pre-trained models on the browser using this library.


BigDL is distributed deep learning library for Apache Spark and is designed to scale very well. With the help of BigDL, you can run your deep learning applications directly on Spark or Hadoop clusters, by writing them as Spark programs. It has a rich deep learning support and uses Intel’s Math Kernel Library (MKL) to ensure high performance. Using BigDL, you can also load your pre-trained Torch or Caffe models into Spark. If you want to add deep learning functionalities to a massive set of data stored on your cluster, this is a very good library to use.

[box type=”shadow” align=”” class=”” width=””]Editor’s Note: We have removed Theano and Lasagne from the original list due to the Theano retirement announcement.

RIP Theano!

Before Tensorflow, Caffe or PyTorch came to be, Theano was the most widely used library for deep learning. While it was a low-level library supporting CPU as well as GPU computations, you could wrap it with libraries like Keras to simplify the deep learning process. With the release of version 1.0, it was announced that the future development and support for Theano would be stopped. There would be minimal maintenance to keep it working for the next one year, after which even the support activities on the library would be suspended completely. “Supporting Theano is no longer the best way we can enable the emergence and application of novel research ideas”, said Prof. Yoshua Bengio, one of the main developers of Theano.

Thank you Theano, you will be missed!

Goodbye Lasagne

Lasagne is a high-level deep learning library that runs on top of Theano.  It has been around for quite some time now and was developed with the aim of abstracting the complexities of Theano, and provide a more friendly interface to the users to build and train neural networks. It requires Python and finds many similarities to Keras, which we just saw above. However, if we are to find differences between the two, Keras is faster and has a better documentation in place.[/box]

There are many other deep learning libraries and frameworks available for use today – DSSTNE, Apache Singa, Veles are just a few worth an honorable mention.

Which deep learning frameworks will best suit your needs? Ultimately, it depends on a number of factors. If you want to get started with deep learning, your safest bet would be to use a Python-based framework like Tensorflow, which are quite popular. For seasoned professionals, the efficiency of the trained model, ease of use, speed and resource utilization are all important considerations for choosing the best deep learning framework.

Data Science Enthusiast. A massive science fiction and Manchester United fan. Loves to read, write and listen to music.


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