Neural Networks for the Internet of Things (Synchronous e-Learning)
Training Provider: NATIONAL UNIVERSITY OF SINGAPORE
Course Reference: TGS-2025057486
S$950
Original: S$1,900
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About This Course
At the end of the course, participants will be able to design neural networks to learn and make predictions and decisions about IoT data
What You'll Learn
In this course, participants will learn the basics of neural networks, covering both unsupervised and supervised networks. Topics covered include unsupervised methods like Hebbian Learning, Kohonen Self Organizing Maps and K-Means Clustering, and supervised methods like the Perceptron Learning Law and Gradient Descent. Participants will also look at other forms of gradient descent, and the key issues in designing neural networks to get the best performance, including sizing the network, selecting loss and optimization methods, and overcoming under and overfitting.
Entry Requirements
Minimum of a Diploma with Programming knowledge. Participants must complete the Statistical Models for the Internet of Things
Course Details
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