Based on the Gluon API specification, the new Gluon library in Apache MXNet provides a clear, concise, and simple API for deep learning. It makes it easy to prototype, build, and train deep learning models without sacrificing training speed. Install the latest version of MXNet to get access to Gluon by either following these easy steps or using this simple command:
pip install mxnet --pre --user
- Simple, Easy-to-Understand Code: Gluon offers a full set of plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers.
- Flexible, Imperative Structure: Gluon does not require the neural network model to be rigidly defined, but rather brings the training algorithm and model closer together to provide flexibility in the development process.
- Dynamic Graphs: Gluon enables developers to define neural network models that are dynamic, meaning they can be built on the fly, with any structure, and using any of Python’s native control flow.
- High Performance: Gluon provides all of the above benefits without impacting the training speed that the underlying engine provides.
The community is also working on parallel effort to create a foundational resource for learning about machine learning. The Straight Dope is a book composed of introductory as well as advanced tutorials – all based on the Gluon interface.
- Learn about machine learning basics
- Develop and train a simple neural network model
- Implement a Recurrent Neural Network (RNN) model for Language Modeling
Simple, Easy-to-Understand Code