Deep Learning Framework for Daily Use

Let's dive deeper into the deep learning framework for daily purposes

In the present generation, the world is shifting towards machine learning and artificial intelligence to enhance their business and hold top positions in the market. Companies are adopting machine learning and AI to get intelligent solutions without hard work and predict customer personalization's accordingly. However, specific organizations might not include AI and machine learning for various reasons. The inclusion of a deep learning framework in your business might improve your business wealth. Let's have a brief overview of a few deep-learning frameworks for daily use.

TensorFlow

Google has developed a modern deep-learning framework known as TensorFlow, which supports data processing languages like Python and R. The formation of a network is essential, as one should have an idea about the workflow in neural networks. TensorFlow was initially released on November 9, 2015. Along with this framework, you also acquire the Tensor Board of data visualization, which simplifies the process of visually displaying data. It can be used on Android, Linux, macOS, and Windows platforms.

Keras

Francois Chollet developed Keras, which has 350000+ users, crafting it into one of the rapidly expanding deep learning frameworks. The inclusion of Keras in your organization helps the high-level neural network API, which was written in Python and is used in numerous startups, research labs, and organizations, including Netflix and CNTK. It was released on March 27, 2015, and can be used on cross-platforms. One of the critical features of Keras is that it offers modularity in the form of a sequence with fully customized modules.

PyTorch

Facebook's AI research labs initially developed PyTorch, which was later authorized by Adam Paszke, Sam Gross, Soumith Chintala, and Gregory Chanan. It was created on the Lua-based scientific framework, which was derived from machine learning and deep learning algorithms. PyTorch includes languages such as Python, CUDA, C, and C++ libraries for processing the data. It enables flexible distributed learning and performance improvement in research and production. PyTorch was released in September 2016 and can be used on platforms IA-32 and x86-64.

Theano

The University of Montreal developed the Theano network. It is written in Python and allows users to integrate it with GPS. The presence of Python libraries enables the user to define, improve, and analyze mathematical expressions involving multi-dimensional arrays. Moving to the stable release, Theano was released on July 27, 2020, and can be used on platforms such as Linux, macOS, and Windows.

Deeplearning4j (DL4j)

A machine learning group, including Adam Gibson, Alex D. Black, Vyacheslav Kokorin, and Josh Patterson, developed a deeplearning4j neural network. It was written in Python, C, C++, and DL4j and supports numerous neural networks, such as CNN and RNN. Deeplearning4j was released on September 10, 2019, and used cross-platforms. A Java and Scala compatible n-dimension array class utilizing ND4J enables advanced scientific computing in your organization or company.