When comparing the two most popular deep learning frameworks, one of the first things you should know is the language they’re written in. Python is the fastest-growing programming language, and it’s the preferred language for most machine learning and artificial intelligence work. Python also has a Pythonic frontend, called PyTorch, and it can be extended with Python packages. It is also easier to use than other deep learning frameworks, thanks to its simple syntax.
Keras was originally written by Francois Chollet. It has a simple, intuitive user interface and supports both convolutional and recurrent networks. Theano is also supported by several programming languages, including Python. Then, there’s TensorFlow, which is a popular deep learning framework for Python developers. Regardless of the language you use, both frameworks offer easy-to-use and robust support for machine learning applications.
On CNN, Tensorflow and Chainer have similar running times. On the CIFAR-100, Tensorflow has the lowest accuracy rate, while MXNet has the fastest training time. Chainer and Tensorflow, however, are very similar when compared. Their training times are comparable, and they’re growing faster than each other. But which framework is growing the fastest? And how can you tell which one’s best?
While there’s a wide range of deep learning frameworks on the market, two stand out from the rest. The MXNet deep learning framework has been used by many big companies and is based on several different programming languages. It supports both asynchronous and synchronous training. It’s also versatile and supports several GPUs and enables distributed and multi-GPU training. So which Deep Learning Framework is Growing Fastest?
Both TensorFlow are gaining popularity as deep learning technologies continue to grow. Both have advantages and disadvantages, but TensorFlow has a strong community and is industry-relevant. It also supports embedded computer vision and is easily deployable to various platforms, including Android devices. Its versatility has made it the first choice of many companies for large-scale applications. Its scalability and versatility have also made it one of the most popular deep learning frameworks.
TensorFlow is the most widely used deep learning framework and has the largest developer community. Its recent open sourcing of two development boards and a mobile application, TensorFlow Lite, and Sparkfun make it one of the most popular deep learning frameworks. TensorFlow supports multiple languages including Python, C++, Java, Go, and R. If you’re a Python or Scala developer, TensorFlow is a great choice for you.
Deeplearning4j is another popular deep learning framework. Its native Java code targets the Java Virtual Machine. It has been donated to the Eclipse Foundation in October 2017. DL4J is an open source deep learning library developed in Java but offers good support for other programming languages and frameworks. Additionally, it supports CUDA and OpenMP, which are widely used in distributed environments. It is easy to integrate with both Python and Java.
Theano is the granddaddy of deep learning frameworks. It was developed by researchers at Carnegie Mellon University of Washington. Amazon is one of the biggest customers of Theano. Most pre-built models on Amazon SageMaker are built using this framework. Caffe2 is a successor to Caffe, and is claimed to be scalable and lightweight. It also features a C++ engine and Python API.
While the accuracy of TensorFlow, Chainer, and MXNet are comparable to each other, Theano has a higher peak accuracy than MXNet and Keras. MXNet and Chainer achieve similar accuracy, though it takes them longer to reach the peak. Theano and PyTorch also have the highest peak accuracy, but are more volatile. Theano achieves its peak accuracy at the 20th epoch.