Cursos de Building Deep Learning Models with Apache MXNet

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Código del Curso

mxnet

Duración

21 horas (usualmente 3 días, incluidas las pausas)

Requerimientos

  • An understanding of machine learning principles
  • Python programming experience

Audience

  • Data scientists

Descripción General

MXNet is a flexible, open-source Deep Learning library that is popular for research prototyping and production. Together with the high-level Gluon API interface, Apache MXNet is a powerful alternative to TensorFlow and PyTorch.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Apache MXNet to build and deploy a deep learning model for image recognition.

By the end of this training, participants will be able to:

  • Install and configure Apache MXNet and its components.
  • Understand MXNet's architecture and data structures.
  • Use Apache MXNet's low-level and high-level APIs to efficiently build neural networks.
  • Build a convolutional neural network for image classification.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.

Programa del Curso

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

Testimonios

★★★★★
★★★★★

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