• Online, Self-Paced
Course Description

Learners can explore the prominent machine learning elements that are used for computation in artificial neural networks, the concept of edge detection, and common algorithms, as well as convolution and pooling operations, and essential rules of filters and channel detection, in this 10-video course. Key concepts covered here include the architecture of neural networks, along with essential elements used for computations by focusing on Softmax classifier; how to work with ConvNetJS as a Javascript library and train deep learning models; and learning about the edge detection method, including common algorithms that are used for edge detection. Next, you will examine the series of convolution and pooling operations used to detect features; learn the involvement of math in convolutional neural networks and essential rules that are applied on filters and channel detection; and learn principles of convolutional layer, activation function, pooling layer, and fully-connected layer. Learners will observe the need for activation layers in convolutional neural networks and compare prominent activation functions for deep neural networks; and learn different approaches to improve convolution neural networks and machine learning systems.

Learning Objectives

{"discover the key concepts covered in this course"}

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Systems Architecture