Redirecting to default login... Supervised Neural Networks & Deep Learning – Mu Sigma University Learning Management System

Course

Supervised Neural Networks & Deep Learning

This course will enable learners to understand key concepts of supervised learning through Neural Networks and Deep Learning. At the end of this course, learning will also be able to understand their applications in image classification, speech recognition, machine translation and other areas and be able to distinguish these from conventional applications in forecasting, classification, and regression.

6 Lessons

Outcomes By the end of the course, learners will be able to: Distinguish the need behind Neural Networks (NN) and Deep Learning compared to existing statistical techniques Comprehend the architecture, applications, functionalities, nuances of a NN and various Python frameworks to implement them Get familiarized with supervised Deep Learning methods like Artificial Neural Networks (ANN), Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN) along with their use cases and enhancement techniques	Level:	03
	Duration:	12 Hours
	Pre-requisites:	Refer Topics on Mathematics
	What’s next:	TBD

Lessons

Getting started: Abbreviations & Python libraries

Introduction to Neural Networks and Deep Learning

Supervised Deep Learning- Artificial Neural Networks (ANN)

Supervised Deep Learning- Convolutional Neural Networks (CNN or ConvNet)

Supervised Deep Learning- Recurrent Neural Networks (RNN)

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