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Convolutional Neural Networks: Fundamentals

Convolutional Neural Networks: Fundamentals


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Explore the concept of convolutional neural network (CNN) and its underlying architecture. Discover the principles and methods driving CNN, including parameter sharing, spatial extents, padding, strides, and pooling layers.



Expected Duration (hours)
0.8

Lesson Objectives

Convolutional Neural Networks: Fundamentals

  • illustrate the concept of visual signal perception using a biological example
  • describe convolutional neural network, its architecture, and its layers
  • describe the driving principles of convolutional neural network
  • describe the combined approach of implementing convolutional layer and sparse interaction
  • describe shared parameters and spatial in a convolutional neural network (CNN)
  • describe convolutional padding and strides in a convolutional neural network (CNN)
  • recognize the relevance and importance of pooling layers in convolutional neural networks (CNNs)
  • use ReLU on convolutional neural networks (CNNs)
  • define semantic segmentation and its implementation using Texton Forest and random-based classifier
  • describe gradient descent and list its prominent variants
  • list CNN layers, implementation approaches, layers, and variants of gradient descent
  • Course Number:
    it_mlodcndj_01_enus

    Expertise Level
    Intermediate