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