It also supports data parallelism and contains many pre-trained models. NumPy-Esque syntax has been used to implement this library in python. It is available on Linux, macOS, Windows, Android, and iOS platforms. Torch is one of the oldest frameworks which provides a wide range of algorithms for deep machine learning. Some libraries have been around for quite some time, and some have been launched very recently. The codes are written in Python on top of CUPY and Numpy libraries. Prominent companies like Airbus, Google, IBM and so on are using TensorFlow to produce deep learning algorithms. Being run by Google means that it will stay around for a while, making it a safe investment. Which one is better? Yangqing Jia is the creator of Caffe2, who now works at Facebook. The CNTK has both low level and high-level API for building neural networks. Tensorflow is another library that is free and open-source which could be used to implement dataflow in the program. These include the more user-friendly frameworks- Keras, Lasagne, and Blocks. While developing the algorithms that need moderate system configuration, Theano can be used without any doubt. -Theano provides error messages, but those messages are cryptic. Theano has been developed by the LISA group which is a part of the varsity of Montreal while Tensorflow has been developed by the Google Brain team for internal use. In a statement released by its developer, Yoshua Bengio, on 28th September 2017, he stated that the development of Theano would cease after its 1.0 version update. It comes with detailed documentation, which makes it good to use for both beginners and experienced users. Both of them are developed for the same purpose but due to the role of organizations, they hold the label of reliability with them. However, it can be a bit difficult to set it up in CentOS. It is efficient and user friendly due to the use of LuaJit, the scripting language, which provides maximum flexibility to the user. On a Concluding Note, it can be said that both APIs have a similar Interface. TensorFlow vs. Theano- which one is right for you? It is based on the languages of Python and C++ and is multi-GPU. It makes the efficient use of a single CPU and generates the outcome which is based on the processing power of the CPU. But TensorFlow is comparatively easier yo use as it provides a lot of Monitoring and Debugging Tools. -Theano provides error messages, but those messages are cryptic. Please feel free to reach out to us, if you have any questions. Let's discuss this. The following are some of the key differences that are mentioned below: Theano has been developed by the LISA group which is a part of the varsity of Montreal while Tensorflow has been developed by … To cater to this growing need, numerous deep learning libraries have been developed. In addition to this, it is capable of working with multiple CPUs. It is used to being the feature of artificial intelligence by making the use of python. -Theano has been around since 2007, and TensorFlow has been around since 2017. Final Verdict: Theano vs TensorFlow. As Theano runs on CPU and GPU, it is much faster than Python itself, providing a much faster and efficient operation. However, there is no commercial support for it. Numerous other open-source deep libraries have been built on top of Theano, such as Keras, Lasagne, and Blocks. Caffe2 is considered to be lightweight. On the other hand, TensorFlow is still available in the market. It all depends on the user's preferences and requirements. The most important reason people chose TensorFlow is: TensorFlow can run with multiple GPUs. The fact that it could make use of multiple CPUs makes it the one that can do complex computations in less time than what is taken by Theano for the same. Furthermore, Torch provides the best packages in machine learning, signal processing, parallel processing, computer vision, video, audio, image, and networking. Privacy Policy and Terms of Use | It lets the user create a neural network that works on a large scale and can be multi-layer. Keras can be used as a high-level Application Programming Interface (API). Theano is ample strong to perform complex computations but sometimes it is not able to meet the requirements due to its low compile speed. Without any further ado, let's discuss these two, along with a few other frameworks. However, Tensorflow tends to be the most famous deep learning framework today. It does not appear to be as widely used at TensorFlow, but this framework is considered to have the potential to have exponential growth in the near future. Tensorflow is the C++ and python based library that means it could be used in both, the C++ and the Python programming. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP. Theano vs Tensorflow has its own importance and their preference is based on the requirements of the application where it has to be used. The battle of the frameworks- Theano vs. TensorFlow. -Theano is written in Python, and TensorFlow is written in C++, Python, CUDA. Chainer is based in Tokyo, with engineers mostly from the University of Tokyo. It is a scripting language program which was initially written in and offered on the Lua programming language but has now been ported to various other languages, such as Python (Pytorch) and C/C++. It is considered to be faster than other Python-based frameworks. -Theano is mostly used in carrying out Mathematical operations, whereas TensorFlow is used in voice/sound recognition, text-based applications, image recognition, time series, and video detection. It is a Google open source project which replaced Theano. -Theano is mostly used in carrying out Mathematical operations, whereas TensorFlow is used in voice/sound recognition, text-based applications, image recognition, time series, and video detection. You can also build arbitrary graphs of neural networks and parallelize them over CPUs and GPUs in the most efficient way possible. It provides multi-GPU and has Lua as its base language. It contributes to artificial intelligence by introducing the use of data flow graphs. Parallel Distributed Deep Learning is a deep learning framework that was created and is supported by Baidu. These three provide high-level frameworks for fast prototyping and model testing.