Building Neural Networks: Development Principles

Neural Networks    |    Intermediate
  • 12 Videos | 1h 25m 48s
  • Includes Assessment
  • Earns a Badge
Likes 11 Likes 11
Explore essential machine learning components used to learn, train, and build neural networks and prominent clustering and classification algorithms in this 12-video course. The use of hyperparameters and perceptrons in artificial neuron networks (ANNs) is also covered. Learners begin by studying essential ANN components required to process data, and also different paradigms of learning used in ANN. Examine essential clustering techniques that can be applied on ANN, and the roles of the essential components that are used in building neural networks. Next, recall the approach of generating deep neural networks from perceptrons; learn how to classify differences between models and hyperparameters and specify the approach of tuning hyperparameters. You will discover types of classification algorithm that can be used in neural networks, and features of essential deep learning frameworks for building neural networks. Explore how to choose the right neural network framework for neural network implementations from the perspective of usage scenarios and fitment model, and define computational models that can be used to build neural network models. The concluding exercise concerns ANN training and classification.

WHAT YOU WILL LEARN

  • identify the key subject areas covered in this course
    describe the essential artificial neural network components that are required for processing data
    recognize the different paradigms of learning that are used in artificial neural network
    list the essential clustering techniques that can be applied on artificial neural network
    recognize the roles of the essential components that are used in building neural networks
    recall the approach of generating deep neural networks from perceptrons
  • classify the differences between models and hyperparameter and specify the approach of tuning hyperparameters
    define the prominent types of classification algorithm that can be used in neural networks
    describe the prominent features of essential deep learning frameworks for building neural networks
    recognize how to choose the right neural network framework for neural network implementations from the perspective of usage scenarios and fitment model
    define the computational models that can be used to build neural network models
    list the essential components of ANN for processing data, recall the clustering techniques that can be applied on ANN, differentiate between models and hyperparameters, and specify the types of classification algorithms that can be used in ANN

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 41s
    UP NEXT
  • Playable
    2. 
    Artificial Neural Network Processing Components
    8m 23s
  • Locked
    3. 
    Learning and Training in Artificial Neural Network
    7m 9s
  • Locked
    4. 
    Cluster Analysis in Artificial Neural Network
    5m 50s
  • Locked
    5. 
    Neural Network Building Blocks
    10m 20s
  • Locked
    6. 
    Perceptron to Deep Neural Network
    7m 52s
  • Locked
    7. 
    Model and Hyperparameter
    7m 37s
  • Locked
    8. 
    Classification with Neural Networks
    6m 15s
  • Locked
    9. 
    Deep Learning Frameworks
    7m 4s
  • Locked
    10. 
    Neural Network Categorization
    5m 14s
  • Locked
    11. 
    Neural Network Computational Model
    8m 11s
  • Locked
    12. 
    Exercise: ANN Training and Classification
    5m 13s

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