Developing AI and ML Solutions with Java: Neural Network and Neuroph Framework Overview/Description Expected Duration Lesson Objectives Course Number Expertise Level Overview/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.
Expected Duration (hours)
1.9
Lesson 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
Course Number: it_sdjaai_03_enus
Expertise Level
Intermediate