Course Outline

Introduction

  • 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

Setup

  • 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

Troubleshooting

Summary and Conclusion

Requirements

  • An understanding of machine learning principles
  • Python programming experience

Audience

  • Data scientists
  21 Hours

Number of participants



Price per participant

Testimonials (5)

Related Courses

Deep Learning AI Techniques for Executives, Developers and Managers

  21 Hours

Deep Learning for Medicine

  14 Hours

Related Categories