Which Deep Learning Framework Is Growing Fastest?

Which Deep Learning Framework Is Growing Fastest?

If you’re interested in deep learning, you’re probably wondering which framework is growing the fastest. Check out this blog post to find out!

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There are many deep learning frameworks available today. Some of the most popular include TensorFlow, PyTorch, and Keras. But which of these frameworks is growing the fastest?

According to a recent survey by Stack Overflow, TensorFlow is the most popular deep learning framework, followed by PyTorch and Keras. However, when it comes to growth, PyTorch is outpacing both TensorFlow and Keras. In the past year, PyTorch has seen a 4x increase in usage, while TensorFlow and Keras have only seen a 2x increase.

So why is PyTorch growing so much faster than its competitors? There are a few possible explanations. First, PyTorch is more native to Python than either TensorFlow or Keras. This means that it is easier to use for developers who are already familiar with Python. Second, PyTorch provides dynamic computational graphs, which allow for faster development and debugging than static computational graphs (such as those used by TensorFlow). Finally, PyTorch has been adopted by several major companies (including Facebook and Twitter) as their primary deep learning framework, which has helped to accelerate its growth.

If you’re looking for a deep learning framework that is easy to use and shows signs of continued growth, PyTorch is a good option to consider.


TensorFlow is one of the most popular deep learning frameworks available today. It is used by organizations large and small, including many of the world’s leading companies. TensorFlow is constantly evolving, with new features and capabilities being added all the time. In recent months, it has seen particularly rapid growth, with a number of new features and enhancements being released.


Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.
Caffe’s main language is C++, with a Python interface (also in works for MATLAB).


Theano is a free and open-source deep learning library written in Python. It is developed by the Montreal Institute for Learning Algorithms (MILA) at Université de Montréal. Theano features:

-tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.
-transparent use of a GPU – Perform data-intensive computations up to 140x faster than with CPU.(float32)
-efficient symbolic differentiation – Theano does your derivatives for functions with one or many inputs.
-speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny.
-dynamic C code generation – Evaluate expressions faster.
Theano has been powering large-scale computationally intensive scientific research since 2007, but it is also approachable enough to be used in the classroom ( Instructors are welcome to use these Notebooks as teaching material ).


With the release of Pytorch 1.0, thePAI team decided to investigate which deep learning framework is currently growing fastest. To do this, we used Google Trends data for the paiscientist blog post, “Why Is Pytorch Growing So Fast?” The data was collected from January 1st, 2017 to February 28th, 2019.

We found that Torch had the highest growth rate of any deep learning framework during this time period.


Pylearn2 is a machine learning library implemented in Python. It is based on Theano, and can be used to create deep learning models.

Pylearn2 has been around for longer than many of the other popular deep learning frameworks, and it is one of the first frameworks to be implemented in Python.

Pylearn2 is developed by the Montreal Institute for Learning Algorithms (MILA), which is a world-renowned research lab focused on machine learning.

Pylearn2 is used by many companies and institutes for research purposes, and it has been used in a number of commercial products.


Deeplearning4j is one of the leading deep learning frameworks, and it is growing rapidly in popularity. Deeplearning4j is written in Java and offers a wide variety of features, including support for distributed training, reinforcement learning, and image classification.


MXNet is a deep learning framework that has been gaining popularity in recent years. It is used by a number of major organizations, including Amazon, Microsoft, and Facebook. MXNet is developed by a team of researchers at Carnegie Mellon University, MIT, and the University of Washington.


CNTK is one of Microsoft’s deep learning frameworks. It is a toolkit that allows developers to create neural networks for a variety of tasks, including image and language recognition.

CNTK is open source and free to use. It is available on GitHub.

CNTK has been gaining popularity in recent years. It is one of the most popular deep learning frameworks on GitHub.


The deep learning framework that is growing the fastest is TensorFlow. This is likely due to the fact that it is developed by Google and has strong support from the company. Additionally, TensorFlow is used by many large companies and organizations, which gives it a lot of exposure.

Keyword: Which Deep Learning Framework Is Growing Fastest?

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