Course details

Building Neural Networks: Development Principles

Building Neural Networks: Development Principles


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

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.



Expected Duration (hours)
1.4

Lesson Objectives

Building Neural Networks: Development Principles

  • 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
  • Course Number:
    it_mlbdnndj_01_enus

    Expertise Level
    Intermediate