Neural Network & Neuroph Framework

Java SE 8    |    Intermediate
  • 16 Videos | 1h 55m 13s
  • Includes Assessment
  • Earns a Badge
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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

  • Playable
    1. 
    Neural Network and its Essential Components
    4m 18s
    UP NEXT
  • Playable
    2. 
    Implement a Simple Neural Network
    8m 1s
  • Locked
    3. 
    Neural Network Types
    3m 45s
  • Locked
    4. 
    Implementing Hopfield Neural Networks
    10m 30s
  • Locked
    5. 
    Implementing Back Propagation Neural Networks
    8m 35s
  • Locked
    6. 
    Role of Activation Function
    5m 11s
  • Locked
    7. 
    Loss Functions and their Benefits
    3m
  • Locked
    8. 
    Implementing Activation Functions and Loss Functions
    10m 13s
  • Locked
    9. 
    Hyperparameter
    12m 3s
  • Locked
    10. 
    Neuroph Java Neural Framework Capabilities
    6m 24s
  • Locked
    11. 
    Hyperparameter Implementation using DL4J
    4m 50s
  • Locked
    12. 
    Deep Learning
    4m 14s
  • Locked
    13. 
    Comparing Deep Learning and Graph Models
    6m 37s
  • Locked
    14. 
    Combining Deep Learning and Graph Model
    12m 12s
  • Locked
    15. 
    Deep Learning and Graph Model Use Cases
    3m 51s
  • Locked
    16. 
    Exercise: Working with Neuroph and Neural Networks
    3m 57s

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