Businesses are looking forward to uplifting their operations by embedding machine learning and artificial intelligence to produce better predictions and analytics. Businesses can achieve their big goals divided into small targets efficiently and effectively with the right deep learning frameworks.

The goal of artificial intelligence on mobile apps is to produce not only apps but smarter and intelligent apps that solve complex problems in the least time frame. However, executing complex solutions that work efficiently is tough to implement, which is also why data scientists are regularly experimenting with many deep learning frameworks.

5 Deep Learning Frameworks

The article covers deep learning frameworks for real problem solvers who want to solve their business problems with the power of deep learning. You can read these frameworks and decide which framework is best for your business requirements.

Keras

Keras has a network library written in Python for quick experimentation for the developers. The library supports the python interface efficiently since it was written in python language, making it capable of running on TensorFlow and Theano. Since TensorFlow does not have a user-friendly interface, Keras is a new and robust framework with a simplistic interface with the ultimate goal of quick prototyping. The framework is lightweight in size containing only essential features required for building a deep learning model for multiple layers. However, Keras is a part of TensorFlow’s API, but it is powerful enough to win several benefits over TensorFlow. The framework wins in tagging, translation, summarizing, and easy classification.

MXNet

High efficiency and productivity are major goals of deep learning frameworks. MXNet was announced with high efficiency and a flexible approach. The framework is supported by multiple languages, not limited to Python like Keras. It also supports Julia, R, and c++, giving users a flexible approach to programming in different languages.

Developers are free to train their deep learning models in applications in any language they are comfortable with. For enterprises, the framework supports many GPUs providing a bag of opportunities for the developers. Speech recognition and forecasting are a few of the capabilities of the MXNet framework providing itself in the deep learning approaches.

Microsoft Cognitive Toolkit

CNTK is Microsoft’s top deep learning framework built with the goal of easy training in deep learning models. The framework is open-source and supports interfaces like the command line, Python, and C++. The image, speech, and text-based operations efficiently provide an effective approach to enterprises.

It is also known for its high-quality performance by smoothly communicating with the resources to implement an easy tool kit. RNN and CNN support is flawless in Microsoft’s cognitive toolkit with all the capabilities of providing solutions to speech recognition problems, handwriting, and more.

Tensor Flow

If you are searching for the best deep learning framework, TensorFlow is your option. A number of company giants like Twitter and IMB use this framework. The reason big brands rely on TensorFlow is its highly flexible system architecture. TensorFlow is efficient like Google Translate with speech recognition, tagging, and forecasting elements.

Developers can use the framework on desktop and mobile with a support of a number of languages like Python and C++. The wrapper libraries are vast in number to bring effective data visualization for better performance.

Caffe

A deep learning framework with the support of MATLAB, C, C++, and Python with a command-line interface proving its high flexibility. It is one of the top deep learning frameworks that supports the most number of programming languages.

Businesses looking to solve image processing problems can enjoy the effectiveness of Caffe, which has a strong library. It is one of the fastest libraries available in the market for deep learning. Its recognition capabilities make it one of the best visual recognizers in the market.

Conclusion

Deep learning is proving its importance in the tech market. The practical use of machine learning and artificial intelligence has proved the importance of deep learning in 2022. The list above provides you with a number of deep learning frameworks that you can use in your business to improve your operations. Entrepreneurs can select the best framework according to their needs and maximize their efficiency for better growth. Directories like AppFirmsReview, provide you a list of top android and ios app development companies who are masters in deep learning. You can choose from their list and hire one of the companies according to your business needs.

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