SkillSoft Explore Course – Lambers, Inc.

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

This 15-video course explores how to design advanced machine learning algorithms by using training patterns, pattern association, the Hebbian learning rule, and competitive learning. First, learners examine the concepts and characteristics of online and offline training techniques in implementing artificial neural networks, and different training patterns in teaching inputs that are used in implementing artificial neural networks. You will learn to manage training samples, and how to use Google Colab to implement overfitting and underfitting scenarios by using baseline models. You will examine regularization techniques to use in training artificial neural networks. This course then demonstrates how to train previously-built neural network models using Python, and the prominent training algorithms to implement pattern associations. Next, learn the architecture and algorithm associated with learning vector quantization; the essential phases involved in implementing Hebbian learning; how to implement Hebbian learning rule using Python; and the steps involved in implementing competitive learning. Finally, you will examine prominent techniques to use to optimize neural networks, and how to debug neural networks.
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Asset ID	it_mltnnndj_02_enus
Course Type	Video Course
Course Category	ML Programmer to ML Architect

Objectives
Training Neural Networks: Advanced Learning Algorithms identify the subject areas covered in this course describe features of online and offline training methods in artificial neural network describe the training patterns and teaching inputs that are used in artificial neural networks describe the approach of managing training samples implement overfitting and underfitting using baseline model describe the regularization techniques used in deep neural network train built models of neural networks using Python to implement prediction with high accuracy list the prominent training algorithms that are used for pattern association describe the architecture along with the algorithm associated with learning vector quantization define the essential phases involved in implementing Hebbian learning implement the Hebbian learning rule using Python describe the steps involved in implementing competitive learning list approaches of optimizing neural networks debug neural networks recall the training algorithms used for pattern association, list the steps of implementing competitive learning, and implement the Hebbian learning rule using Python

Objectives

Training Neural Networks: Advanced Learning Algorithms

identify the subject areas covered in this course
describe features of online and offline training methods in artificial neural network
describe the training patterns and teaching inputs that are used in artificial neural networks
describe the approach of managing training samples
implement overfitting and underfitting using baseline model
describe the regularization techniques used in deep neural network
train built models of neural networks using Python to implement prediction with high accuracy
list the prominent training algorithms that are used for pattern association
describe the architecture along with the algorithm associated with learning vector quantization
define the essential phases involved in implementing Hebbian learning
implement the Hebbian learning rule using Python
describe the steps involved in implementing competitive learning
list approaches of optimizing neural networks
debug neural networks
recall the training algorithms used for pattern association, list the steps of implementing competitive learning, and implement the Hebbian learning rule using Python