SkillSoft Explore Course – Lambers, Inc.

Aspire ML Programmer to ML Architect ML Track 2: DL Programmer

Discover the basics of perceptrons, including single- layer and multilayer, and the roles of linear and nonlinear functions in this 10-video course. Learners will explore how to implement perceptrons and perceptron classifiers by using Python for machine learning solutions. Key concepts covered in this course include perceptrons, single-layer and multilayer perceptrons, and the computational role they play in artificial neural networks; learning the algorithms that can be used to implement single-layer perceptron training models; and exploring multilayer perceptrons and illustrating the algorithmic difference from single-layer perceptrons. Next, you will learn to classify the role of linear and nonlinear functions in perceptrons; learn how to implement perceptrons by using Python; and learn approaches and benefits of using the backpropagation algorithm in neural networks. Then learn the uses of linear and nonlinear activation functions in artificial neural networks; learn to implement a simple perceptron classifier using Python; and learn the benefits of using the backpropagation algorithm in neural networks and implement perceptrons and perceptron classifiers by using Python.

Asset ID	it_mlfdnndj_02_enus
Course Type	Video Course
Course Category	ML Programmer to ML Architect

Objectives
Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms discover the key concepts covered in this course describe perceptrons and the computational role they play in artificial neural networks recognize the algorithms that can be used to implement single layer perceptron training models define multilayer perceptrons and illustrate the algorithmic difference from single layer perceptrons classify the role of linear and non-linear functions in perceptrons demonstrate the implementation of perceptrons using Python describe approaches and benefits of using the backpropagation algorithm in neural networks recognize the uses of linear and non-linear activation functions in artificial neural networks implement a simple perceptron classifier using Python recall the benefits of using the backpropagation algorithm in neural networks, and implement perceptrons and perceptron classifiers using Python

Objectives

Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms

discover the key concepts covered in this course
describe perceptrons and the computational role they play in artificial neural networks
recognize the algorithms that can be used to implement single layer perceptron training models
define multilayer perceptrons and illustrate the algorithmic difference from single layer perceptrons
classify the role of linear and non-linear functions in perceptrons
demonstrate the implementation of perceptrons using Python
describe approaches and benefits of using the backpropagation algorithm in neural networks
recognize the uses of linear and non-linear activation functions in artificial neural networks
implement a simple perceptron classifier using Python
recall the benefits of using the backpropagation algorithm in neural networks, and implement perceptrons and perceptron classifiers using Python