• Online, Self-Paced
Course Description

Discover the essential features and capabilities of Neuroph framework and Neural Networks, and also how to work with and implement Neural Networks using Neuroph framework.

Learning Objectives

Developing AI and ML Solutions with Java: Neural Network and Neuroph Framework

  • recognize the concept of neural network, neurons and the different layers of neuron
  • describe the practical implementation of a simple neural network using Java
  • list the various types of neural networks that are prominently used today
  • Implementing Hopfield Neural Networks
  • describe how to implement back propagation neural networks using Java
  • identify the relevance of activation functions and list the various types of activation functions in neural networks
  • recognize the benefits of loss functions and list the various types of loss functions in practice today
  • implement activation functions and loss functions using DL4J
  • demonstrate how to work with hyperparameters in neural networks
  • recall the capabilities and practical implementation of Neuroph framework
  • work with the Arbiter hyperparameter optimization library designed to automate hyperparameter
  • describe the concept of the deep learning and list its various components
  • recognize the similarities and differences between deep learning and graph model
  • work with the collaboration of deep learning and graph model
  • identify the relevant use cases for implementing deep learning and graph model
  • create and modify a Neuroph project using Neural networks

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Software Development