Introduction
Apache MXNet is an inference framework with easy-to-use features concise application program interface for machine learning.
The Gluon interface initiates deep learning on the cloud or edge devices for skilled and unskilled developers. With the help of Gluon code, developers can build LSTMs for object detection and image recognition.
Developers can start with MXNet on AWS using Amazon SageMaker, which provides building, training, and deployment of machine learning models.
Benefits of Deep Learning
There are various benefits of deep learning using MXNet. Some of them are listed below.
Ease of Implementation Using Gluon
Gluon provides a high-level interface that helps easy learning and deployment of machine learning models. It provides a simple structure that is easy to work with and debug.
Efficient Performance
Deep learning tasks can efficiently be allocated to various GPUs, which provides the developers with the facility to complete large tasks in comparatively less time. Linear Scaling is correlated with the number of GPUs in a cluster. Batch-based inferencing helps developers to save time and increase productivity.
Representation of Edge-Based Neural Networks
MXNet allows developers to represent neural network models that can operate on edge devices like Raspberry Pi or smartphones and helps in processing data remotely.
Flexibility
MXNet supports many programming languages—like C++, JavaScript, Python, R, Matlab, Julia, Scala, Clojure, and Perl. Developers can start with any of the available languages.
At the Backend, the entire code is compiled using the C++ programming language for efficient performance.
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