Course Outline


  • Apache MXNet vs PyTorch

Deep Learning Principles and the Deep Learning Ecosystem

  • Tensors, Multi-layer Perceptron, Convolutional Neural Networks, and Recurrent Neural Networks
  • Computer Vision vs Natural Language Processing

Overview of Apache MXNet Features and Architecture

  • Apache MXNet Compenents
  • Gluon API interface
  • Overview of GPUs and model parallelism
  • Symbolic and imperative programming


  • Choosing a Deployment Environment (On-Premise, Public Cloud, etc.)
  • Installing Apache MXNet

Working with Data

  • Reading in Data
  • Validating Data
  • Manipulating Data

Developing a Deep Learning Model

  • Creating a Model
  • Training a Model
  • Optimizing the Model

Deploying the Model

  • Predicting with a Pre-trained Model
  • Integrating the Model into an Application

MXNet Security Best Practices


Summary and Conclusion


  • An understanding of machine learning principles
  • Python programming experience


  • Data scientists
  21 Hours


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