Neural Network & Neuroph Framework

Java SE 8    |    Intermediate
  • 16 videos | 1h 47m 43s
  • Includes Assessment
  • Earns a Badge
Rating 4.8 of 4 users Rating 4.8 of 4 users (4)
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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • 4m 18s
    After completing this video, you will be able to recognize the concept of neural networks, neurons, and the different layers of neurons. FREE ACCESS
  • 8m 1s
    After completing this video, you will be able to describe the practical implementation of a simple neural network using Java. FREE ACCESS
  • Locked
    3.  Neural Network Types
    3m 45s
    After completing this video, you will be able to list the various types of neural networks that are used today. FREE ACCESS
  • Locked
    4.  Implementing Hopfield Neural Networks
    10m 30s
    Learn how to implement Hopfield neural networks. FREE ACCESS
  • Locked
    5.  Implementing Back Propagation Neural Networks
    8m 35s
    After completing this video, you will be able to describe how to implement back propagation neural networks using Java. FREE ACCESS
  • Locked
    6.  Role of Activation Function
    5m 11s
    In this video, you will learn about the activation functions and the different types of activation functions used in neural networks. FREE ACCESS
  • Locked
    7.  Loss Functions and their Benefits
    3m
    After completing this video, you will be able to recognize the benefits of loss functions and list the various types of loss functions in practice today. FREE ACCESS
  • Locked
    8.  Implementing Activation Functions and Loss Functions
    10m 13s
    In this video, you will learn how to implement activation functions and loss functions using DL4J. FREE ACCESS
  • Locked
    9.  Hyperparameter
    12m 3s
    In this video, you will learn how to work with hyperparameters in neural networks. FREE ACCESS
  • Locked
    10.  Neuroph Java Neural Framework Capabilities
    6m 24s
    Upon completion of this video, you will be able to recall the capabilities and practical implementation of the Neuroph framework. FREE ACCESS
  • Locked
    11.  Hyperparameter Implementation using DL4J
    4m 50s
    In this video, you will work with the Arbiter hyperparameter optimization library designed to automate hyperparameter optimization. FREE ACCESS
  • Locked
    12.  Deep Learning
    4m 14s
    Upon completion of this video, you will be able to describe the concept of deep learning and list its various components. FREE ACCESS
  • Locked
    13.  Comparing Deep Learning and Graph Models
    6m 37s
    Upon completion of this video, you will be able to recognize the similarities and differences between deep learning and graph models. FREE ACCESS
  • Locked
    14.  Combining Deep Learning and Graph Model
    12m 12s
    Learn how to work with deep learning and graph models in collaboration. FREE ACCESS
  • Locked
    15.  Deep Learning and Graph Model Use Cases
    3m 51s
    In this video, you will identify the relevant use cases for implementing deep learning and graph models. FREE ACCESS
  • Locked
    16.  Exercise: Working with Neuroph and Neural Networks
    3m 57s
    In this video, you will create and modify a Neuroph project using neural networks. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE

Course Introduction to Neural Networks February 2024: Session 4 Reply
Rating 3.0 of 1 users Rating 3.0 of 1 users (1)
Course Introduction to Neural Networks February 2024: Session 1 Reply
Rating 5.0 of 1 users Rating 5.0 of 1 users (1)
Course Introduction to Neural Networks February 2024: Session 3 Reply
Rating 1.0 of 1 users Rating 1.0 of 1 users (1)

PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Course Java SE 13: More Operations on Member Variables & Methods
Rating 4.4 of 22 users Rating 4.4 of 22 users (22)
Course Computer Vision: Introduction
Rating 4.4 of 65 users Rating 4.4 of 65 users (65)
Course TensorFlow: Introduction to Machine Learning
Rating 4.2 of 35 users Rating 4.2 of 35 users (35)