Today we are going to help you to find out the alternatives of TensorFlow. TensorFlow is a library that helps in machine learning and artificial intelligence to improve the quality of the search engine. It is an end-to-end open-source, cross-platform library that was developed by the Google brain team in the year of 2015.
The importance of TensorFlow is, when you are looking for automatic suggestions on a large scale, Google will not be able to fulfill your demand. Here TensorFlow comes into the picture. The alternatives of TensorFlow are nothing but deep learning libraries. So, in this article, we have prepared a list of the top 10 alternatives after a long market study.
Top 10 Alternatives of TensorFlow
Here in this section, we are going to discuss the top 10 popular alternatives to TensorFlow. Let’s have a glance.
It is a machine learning software library. The main focus behind the making of this library is to provide easy usages, give scalability an increase it helps in machine learning to provide easy access to the users by providing suggestions.
It also offers high flexibility and strong performance. It is very easy to use, which makes it popular for new users. mlpack was released in 2008, and the latest stable version of this software has marketed in June 2021.
Also, mLpack provides template classes of GRU, LSTM structures. Engineers and researchers use this kind of software.
It is an open-source library that follows a neural network framework. It uses C and CUDA programming language and is a fast, easy-to-install library, which supports the CPU and GPU.
Also, Darknet is very helpful software for research purposes. This library is relatively new in comparison to the others, but it has gained its popularity so fast, probably due to its faster-installing process.
It is based on the decision tree library and an open-source library. Yandex researchers developed it and released it initially in the year 2017. The programming languages are Java, C++, and Python.
CatBoost is a fast, scalable, high-performance gradient booster library, and it also supports CPU, GPU. Things that are positive for the users of CatBoost are, it is more accurate than the other libraries.
It is easy to use gradient boosting, which reduced the need for extensive hyper-parameter tuning. Also, it is extensible, which allows the users to customize the settings without much loss in function.
Theano is an open-source project which has developed by the University of Montreal, Quebec and the license is provided by BSD. It is written in Python, CUDA. Also, it operates in Linux, macOS, Microsoft windows.
Theano is used to optimize the complication of mathematical expressions, especially matrix calculations. It expresses the computations that require a NumPy syntax. The main problem with Theano is, it couldn’t be learned directly, as it is very deep in learning. But we also have a solution for that.
Python has launched the made-easy project for the Theano learners; we recommend you to use these projects. These projects will provide you with an easy data structure with Python, which will help if you are a Theano developer. Another platform, the Lasagne library provides the classes for Theano learning; you may take those classes also.
Keras is also a Python-based open-source neural network library. This is the most important pick in our list. It can run on the upper edge of all the previously mentioned libraries.
Keras provides the best cognitive load reduction practices along with next-level extensibility. Users can combine to create new modules such as ad neural layers, cost functions, optimizers, and many more. Also, these modules are easy to add, and the Python code defines it.
The attractive parts of Keras are its guiding principles and availability of Keras on Android and iOS, which allows the users to use this on smartphones. It also supports GPU and TPU, especially in conjunction with CUDA.
The Torch is an open-source machine learning framework which has specially designed for scientists. It uses the programming language LUA. It has licensed under BSD. The Torch was first time marketed in the year 2002.
The positive part of the Torch is that it provides a wide range of deep algorithms. Also, it uses scripting languages like LuaJIT and C, which makes it better than the competitors.
Torch also offers a flexible N-dimensional powerful array and widely supports GPU. It is a simple and efficient language. Ronan Collobert is the programmer of this language. Also, it is very easy to use.
Microsoft released this in the year of 2008. It is an Infer.NET model-based machine learning environment where the supportive language is C#; it is a cross-platform library.
Infer.NET operates in Windows, macOS, and Linux. Also, the program offered by this library has complied with a high-performance code framework.
This feature allows it to offer substantial scalability, approximate determination, availability of real-time data, and many more attractive features. Also, it has licensed under MIT. It has launched its latest stable version in the year 2019.
8. Scikit Learn
This was released in the year 2007. It is an open-source library based on Matplotlib. Scikit Learn was developed by David Cournapeau and is licensed under BSD.
The latest version has released in the year 2021. Like the other libraries, it is also based on Python and C++. This library focuses more on data modeling rather than data loading and manipulating.
9. Training Mule
Training Mule allows the labeling of images easier as it offers a set of databases for best results. It finds use in hosting the network and offers easy access to handle the model in the cloud. It is also known as Mulesoft training, and it’s a Java-based library.
10. Cloud AutoML
Cloud AutoML is a machine learning service provided by Google. It helps developers with limited machine learning expertise to train high-quality models as per their business needs.
This product is presented by Google and Google’s state-of-art transfer learning and architecture search technology is the key to run this library. It also provides a free version.
We have mentioned all of the popular alternatives in this article. Each has some pros and cons. So, pick the best one as per your need. Hope this will help you.