Convolutional Neural Networks: Fundamentals

Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level

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Overview/Description

Learners can explore the concepts of convolutional neural network (CNN); the underlying architecture, principles, and methods needed to build a CNN; and its implementation in a deep neural network. In this 12-video course, you will examine visual perception, and the ability to interpret the surrounding environment by using light in the visible spectrum. First, learn about CNN architecture; how to analyze the essential layers; and the impact of an initial choice of layers. Next, you will learn about nonlinearity in the first layer, and the need for several pooling techniques. Then learn how to implement a convolutional layer and sparse interaction. Examine the hidden layers of CNN, which are convolutional layers, ReLU (rectified linear unit) layers, or activation functions, the pooling layers, the fully connected layer, and the normalization layer. You will examine machine learning semantic segmentation to understand an image at the pixel level, and its implementation using Texton Forest and a random based classifier. Finally, this course examines Gradient Descent and its variants.

Expected Duration (hours)
0.8

Lesson Objectives

Convolutional Neural Networks: Fundamentals

Course Overview

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