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Top 5 Machine Learning frameworks

Machine learning paradigm is evolving. Machine learning uses algorithms to make computers learn without being programmed. The machine learning frameworks play an important role in web development. In this article, we have described the best machine learning frameworks.

All the frameworks have unique features. Some frameworks are mathematically oriented and others provide a rich set of linear algebra tools.

1. Tensorflowtensorflow machine learning framework

Tensorflow is popular Machine Learning frameworks for Java development. The Google Brain team developed it. They released on 9th November 2015. Tensorflow is available on desktop and mobile. It supports languages like Python, C++, and R to create deep learning models.

The Tensorflow is a highly flexible system. It provides users with multiple models and versions of the same model. It is the most bifurcated Machine Learning project on GitHub. Tensorflow has the largest participation of taxpayers.

2. Caffe 2

caffe 2 machine learning framework

Caffe 2 is an open source framework. Berkley AI Research team developed it. The Caffe is used in the academic research projects and to design startups Prototypes. It is one of the fastest ways to apply deep neural networks to the problem.

It offers the Model zoo. The model zoo is a set of pre-trained models that don’t require any coding to implement. Caffe 2 is suitable for the construction of applications and intended for artificial vision. The Caffe can process over 60M images per day with a single NVIDIA K40 GPU. It is the fastest convent implementations available.

3. Keras

keras machine learning framework

Keras is a Python deep learning library. It is built to provide a simplistic interface. This framework is lightweight, easy to use and straightforward to build a deep learning model.

The Keras framework supports computation with CPUs and GPUs. According to some users, Keras is hard to customize. People use Keras for classification, text generation, tagging, and summarization.

4. Pytorch & Torchpytorch machine learning framework

Pytorch & Torch – a deep learning framework is all about experimentation, flexible and customization. Facebook, Twitter, and Google are using this framework. Pytorch runs on Python. Anyone with a basic understanding of Python can start building their own deep learning models.

This framework’s architectural style and deep modeling process are simple and transparent. It supports DCGs – dynamic computation graphs. Pytorch is fairly immature. It becomes difficult to find information about it.

5. Apache Mahout

apache mahout machine learning framework

Apache Mahout is designed for statisticians, data scientists, and mathematicians. This framework focuses on grouping, filtering, and classification. It supports CPU and GPU operations. The Mahout gives you the ability to develop mathematical calculations in an interactive environment.

This framework climbs onto the Apache Hadoop platform using the map. It does not restrict contributions to other implementations based on Hadoop.

Conclusion

There are many types of machine learning libraries available in the market. You can choose one according to your application, your preferred programming language and the maturity of the framework. Consider all vital factors while choosing a framework. After that, you can hire skilled machine learning web developers from a reliable web development company.

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